UseCases/Sales_Forecasting_UAF/Sales_Forecasting_UAF_PY_SQL.ipynb

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   "source": [
    "<header>\n",
    "   <p  style='font-size:36px;font-family:Arial; color:#F0F0F0; background-color: #00233c; padding-left: 20pt; padding-top: 20pt;padding-bottom: 10pt; padding-right: 20pt;'>\n",
    "       Sales Forecasting using Teradata UAF (Unbounded Array Framework)\n",
    "  <br>\n",
    "       <img id=\"teradata-logo\" src=\"https://storage.googleapis.com/clearscape_analytics_demo_data/DEMO_Logo/teradata.svg\" alt=\"Teradata\" style=\"width: 125px; height: auto; margin-top: 20pt;\">\n",
    "    </p>\n",
    "</header>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca71e2d0-a1c4-410f-af0c-d47aa29e3875",
   "metadata": {},
   "source": [
    "<p style='font-size:20px;font-family:Arial;color:#00233C'><b>Introduction</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Predicting future sales is crucial for any company as it helps in making informed business decisions. However, these sales are usually impacted by a plethora of reasons including seasonality, sales, macro-economic conditions throughout the year which can result in sales being significantly higher or lower than average. This can negatively impact future revenue if sales are not accurately predicted.</p>\n",
    "\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Solution</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We forecast the amount of future sales by developing a time-series modelling pipeline on sales data. The demo shows the power of Vantage through its In-DB analytics time-series capabilities which provides a comprehensive suite of functions most commonly used by Data Scientists across the industry in forecasting pipelines including but not limited to the following standard activities;</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Data preparation: Resampling, convert irregular to regular</li>\n",
    "    <li>Data exploration: Detect stationarity and periodicity</li>\n",
    "    <li>Eliminate Non-stationarity: Seasonal normalizing</li>\n",
    "    <li>Formulate candidate models: ARIMA</li>\n",
    "    <li>Goodness of Fit: Parameter estimates, hypothesis statistical tests</li>\n",
    "    <li>Model Validation: ARIMA Validate</li>\n",
    "    <li>Model Forecasting: Unnormalize, and forecast on test data</li>\n",
    "</ul>\n"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "08b3387e-c800-48c8-8285-438c8357cd8d",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Sales Forecasting Demo Data</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Features</b>: Other exogenous features related to store and environment for time-series analysis</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Train</b>: Weekly sales input data for time-series analysis</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Test</b>: Weekly sales test data for time-series model testing</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Store</b>: Anonymized information about the 45 stores, indicating the type and size of the store</p>\n",
    "\n",
    "      \n",
    "![Dataset%20Description-3.PNG](attachment:Dataset%20Description-3.PNG)\n",
    "    \n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Unbounded Array Framework (UAF) Specifications</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Unbounded Array Framework (UAF) is the Teradata framework for building end-to-end time series forecasting pipelines. It takes the following as inputs.</p>"
   ]
  },
  {
   "attachments": {
    "Input%20Description.PNG": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "67246ef8",
   "metadata": {},
   "source": [
    "![Input%20Description.PNG](attachment:Input%20Description.PNG)\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further information, please <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Unbounded-Array-Framework-Specifications'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b470ccde-f18d-4318-a1d5-2de97d35f493",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Downloading and installing additional software needed</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fdfd271-f281-4a22-a367-38371e4bb7fd",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Here, we import the required libraries, set environment variables and environment paths (if required).</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "060b1f7b-0233-402b-8d7d-d97adbf2bbdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Python libraries\n",
    "import csv\n",
    "import getpass\n",
    "import io\n",
    "\n",
    "# Third-party libraries\n",
    "import pandas as pd\n",
    "import sqlalchemy\n",
    "from sqlalchemy import event\n",
    "from collections import OrderedDict\n",
    "from PIL import Image\n",
    "\n",
    "# Teradata related imports\n",
    "import teradataml as tdml\n",
    "from teradataml import *\n",
    "from teradatasqlalchemy.types import *\n",
    "from teradataml import to_numeric\n",
    "\n",
    "# Modify the following to match the specific client environment settings\n",
    "display.max_rows = 5\n",
    "configure.val_install_location = 'val'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad30cdbd-d022-4a13-8de5-257a64134469",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>1. Initiate a connection to Vantage</b>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>You will be prompted to provide the password. Enter your password, press the Enter key, and then use the down arrow to go to the next cell.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3e2b68d0-69ee-4ee2-9c1b-d4333fb968f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "%run -i ../startup.ipynb\n",
    "eng = create_context(host = 'host.docker.internal', username='demo_user', password = password)\n",
    "print(eng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81480d79-9506-4533-812c-95bf659992c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "execute_sql('''SET query_band='DEMO=Sales_Forecasting_UAF_PY_SQL.ipynb;' UPDATE FOR SESSION;''')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee500f5a-adb1-495f-8d93-b9c574de9574",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Begin running steps with Shift + Enter keys. </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1afed13b-66a4-43e6-822f-71521088c34a",
   "metadata": {},
   "source": [
    "<p style = 'font-size:20px;font-family:Arial;color:#00233C'><b>Getting Data for This Demo</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We have provided data for this demo on cloud storage. You can either run the demo using foreign tables to access the data without any storage on your environment or download the data to local storage, which may yield faster execution. Still, there could be considerations of available storage. Two statements are in the following cell, and one is commented out. You may switch which mode you choose by changing the comment string.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "16973665-4402-466e-bd51-8b3e279bb866",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %run -i ../run_procedure.py \"call get_data('DEMO_SalesForecastingUAF_cloud');\"        # Takes 1 minute\n",
    "%run -i ../run_procedure.py \"call get_data('DEMO_SalesForecastingUAF_local');\"        # Takes 2 minutes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48735cea-1a2a-4bfd-b38a-9975bc2814dc",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Next is an optional step – if you want to see the status of databases/tables created and space used.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85e6f604-ef55-4503-88ac-7dd584fbd601",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%run -i ../run_procedure.py \"call space_report();\"        # Takes 10 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc5f25f8-7100-477a-8e0f-cf7381679363",
   "metadata": {
    "tags": []
   },
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>2. Loading and Preparing Data</b>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Create dataframes for Sales, Features, and Stores tables in the Vantage Advanced SQL Engine</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75bab851",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:11:19.278562Z",
     "start_time": "2022-12-15T17:10:56.508222Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "sales_data = DataFrame(in_schema('DEMO_SalesForecastingUAF', 'Weekly_Sales'))\n",
    "feature_data = DataFrame(in_schema('DEMO_SalesForecastingUAF', 'Features')).drop(['IsHoliday'], axis=1)\n",
    "store_data = DataFrame(in_schema('DEMO_SalesForecastingUAF', 'Stores'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2f9860",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Join datasets to create the ADS using tdml for basic dataframe manipulations</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "602c6c56",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:11:20.075888Z",
     "start_time": "2022-12-15T17:11:19.284616Z"
    }
   },
   "outputs": [],
   "source": [
    "# Join store_data with sales_data\n",
    "sales_data = sales_data.join(store_data, on='Store', how='left', lsuffix='t1', rsuffix='t2').drop(['t2_Store'], axis=1)\n",
    "sales_data = sales_data.assign(Store=sales_data['t1_Store'])\n",
    "sales_data = sales_data.drop(['t1_Store'], axis=1)\n",
    "\n",
    "# Join feature_data with sales_data\n",
    "sales_data = sales_data.join(feature_data, on=['Store', 'Date'], how='left', lsuffix='t1', rsuffix='t3').drop(['t3_Store'], axis=1).drop(['t3_Date'], axis=1)\n",
    "sales_data = sales_data.assign(Store=sales_data['t1_Store'])\n",
    "sales_data = sales_data.assign(Date=sales_data['t1_Date'])\n",
    "sales_data = sales_data.drop(['t1_Store'], axis=1)\n",
    "sales_data = sales_data.drop(['t1_Date'], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41d6cbb9",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating time series identifier for partitioning.</li>\n",
    "    <li>The series identifier was created by joining the department and store ID which provides a unique ID for every time series.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "daa24353",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:11:22.088738Z",
     "start_time": "2022-12-15T17:11:21.067081Z"
    }
   },
   "outputs": [],
   "source": [
    "# Assign a new column 'idcols' based on string concatenation\n",
    "sales_data = sales_data.assign(idcols=sales_data.Dept.str.strip() + '-' + sales_data.Store.str.strip())\n",
    "sales_data = sales_data.assign(idcols=sales_data.idcols.cast(type_=VARCHAR(10)))\n",
    "\n",
    "# Check the shape of the DataFrame\n",
    "sales_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcfcbf0",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Create a table in Vantage based on a TeradataML DataFrame.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "167b0601",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:11:27.910956Z",
     "start_time": "2022-12-15T17:11:22.094679Z"
    }
   },
   "outputs": [],
   "source": [
    "copy_to_sql(df = sales_data, table_name = \"az_sf_joined\", if_exists = \"replace\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f40511f4-e88c-4fab-aa77-0a7b2e15229a",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b><i>*Please scroll down to the end of the notebook for detailed column descriptions of the dataset.</i></b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7731b81e-e32a-499d-a62b-7c7a9aa7db9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "az_sf_joined = DataFrame('az_sf_joined')\n",
    "az_sf_joined[az_sf_joined[\"idcols\"] == \"40-41\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab2f0e31",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>3. Data Cleaning</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81f0c260-9ab9-4089-bb26-041fe4b78f92",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_OutlierFilterFit</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_OutlierFilterFit function calculates the lower_percentile, upper_percentile, count of rows, and median for the specified input table columns. The calculated values for each column help the TD_OutlierFilterTransform function detect outliers in the input table.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Cleaning-Functions/Handling-Outliers/TD_OutlierFilterFit/TD_OutlierFilterFit-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Cleaning-Functions/Handling-Outliers/TD_OutlierFilterFit/TD_OutlierFilterFit-Output'>refer here</a></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "029b6ddd-96f1-413e-b350-27ae5dff4c83",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Drop Table if exists</li>\n",
    "    <li>Applying the TD_OutlierFilterFit function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecee35bf-b997-4026-a902-4b3e0465bd11",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE outlier_fit AS (\n",
    "    SELECT * FROM TD_OutlierFilterFit (\n",
    "        ON az_sf_joined AS InputTable\n",
    "        USING\n",
    "            TargetColumns ('Weekly_Sales')\n",
    "            LowerPercentile (0.05)\n",
    "            UpperPercentile (0.95)\n",
    "            OutlierMethod ('percentile')\n",
    "            ReplacementValue ('delete')\n",
    "            PercentileMethod ('PercentileCont')\n",
    "    ) AS dt\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('outlier_fit')\n",
    "    execute_sql(query)\n",
    "\n",
    "DataFrame('outlier_fit')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d9d81e3-56ed-45bc-a9d3-ed995e4342d2",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>This output is an intermediate table and will be used as an input in the next step i.e. TD_OutlierFilterTransform.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcc2355-b0b0-4de2-95eb-799269b5a09c",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_OutlierFilterTransform</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Filters outliers from the input table. The metrics for determining outliers come from TD_OutlierFilterFit output. The function uses statistical values from the <b>TD_OutlierFilterFit</b> function and runs it against each column. This function then identifies the odd values which are way above the normal values.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Cleaning-Functions/Handling-Outliers/TD_OutlierFilterTransform/TD_OutlierFilterTransform-Input'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Cleaning-Functions/Handling-Outliers/TD_OutlierFilterTransform/TD_OutlierFilterTransform-Output'>refer here</a></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf382c6c",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating an actual table TD_OutlierFilterTransform which is described in the above step.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6440bef-5754-4959-99e0-4f317ae7c1e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE OutlierFilterTransform AS (\n",
    "    SELECT * FROM TD_OutlierFilterTransform (\n",
    "        ON az_sf_joined AS InputTable PARTITION BY ANY\n",
    "        ON outlier_fit AS FitTable DIMENSION\n",
    "    ) AS dt\n",
    ") \n",
    "WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('OutlierFilterTransform')\n",
    "    execute_sql(query)\n",
    "\n",
    "DataFrame('OutlierFilterTransform')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "650dd26e-a436-47b6-b693-aa90d924cf25",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The outliers have been removed from the data for each of the feature.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Checking if any null values are present after filtering out the outliers.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e54fc019",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-26T06:33:07.190524Z",
     "start_time": "2023-06-26T06:33:06.851176Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT COUNT(*) as \"Count\"\n",
    "    FROM OutlierFilterTransform\n",
    "    WHERE Weekly_Sales IS NULL;\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f9728f0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-11-24T11:59:36.857408Z",
     "start_time": "2022-11-24T11:59:36.833435Z"
    }
   },
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>This shows that there are no null values after filtering out the outliers.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the number of weekly sales after the outliers are dropped.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4c80cd0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-06-26T06:33:07.523577Z",
     "start_time": "2023-06-26T06:33:07.192758Z"
    }
   },
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT COUNT(*) as Weekly_Sales\n",
    "    FROM OutlierFilterTransform\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "795c7007-7e76-4ce3-acd2-e4678a2acd3f",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows count of weekly sales in the transformed dataset.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce9d0022-94a6-4fe8-936c-70924a6a7c8c",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Resample</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Once the outliers are removed, the time series is converted to an irregular series.</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_Resample Transforms converts irregular time series into a regular time series and performs linear interpolation.</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>It can also be used to alter the sampling interval for a time series.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Data-Preparation-Functions/TD_RESAMPLE/TD_RESAMPLE-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Data-Preparation-Functions/TD_RESAMPLE/TD_RESAMPLE-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cffc5e4",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Randomly selecting store and department ID to see if there are any missing timestamps.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f3cca75-2e01-47bd-9789-ed6baa077fa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT *\n",
    "    FROM OutlierFilterTransform\n",
    "    WHERE dept = 59 AND store = 43\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query).sort('times')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5967e6a-eea1-47d1-9171-00d582f6ab96",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Dept. No. 59 and Store No. 43 are showed here as a sample.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Randomly selecting a different store and department ID to see if there are any missing timestamps.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19db3c1c-5bd4-4454-83e9-11384e259cce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-01-11T11:13:38.015551Z",
     "start_time": "2023-01-11T11:13:35.840278Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT *\n",
    "    FROM OutlierFilterTransform\n",
    "    WHERE dept = 59 AND store = 5\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query).sort('times')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f557d3-ad62-4360-9709-80619c4b1f5a",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Dept. No. 59 and Store No. 5 are shown here as a sample.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Subtracting the two time series of specific department and store ID's to check the missing time stamps between these two, because we are assuming that if these department and store ID's have missing time stamps then the rest of the ID's will follow the same pattern.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e18a32c-e7f5-4d15-8db6-b2d49d739e9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT times\n",
    "    FROM OutlierFilterTransform\n",
    "    WHERE dept = 59 AND store = 43\n",
    "\n",
    "    MINUS\n",
    "\n",
    "    SELECT times\n",
    "    FROM OutlierFilterTransform\n",
    "    WHERE dept = 59 AND store = 5;\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a0a2c62-857b-4c54-ac5c-e843261373ae",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows that there are missing timestamps between Store No. 43 and Store No. 5. We can say that there can be missing time stamps in rest of the store as well.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Finding the minimum date, so that we can re-sample the dates from this date onwards.</li>\n",
    "    <li>Printing the minimum date</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06891203-a144-4ffc-8f53-1caa378db380",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame.from_query('SELECT MINIMUM(times) as minimum_time FROM OutlierFilterTransform;')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fbcfe7e-2dcf-461e-be88-6066ba23ca30",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The minimum date is extracted here so as to use it in the next step.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Calling the resample function to convert irregular time series into a regular time series</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d37bb39f-1f06-40a2-8df8-17c8a99130fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(ts_sf_resampled_ads_tw)\n",
    "\n",
    "TD_RESAMPLE(\n",
    "    SERIES_SPEC( \n",
    "         TABLE_NAME(OutlierFilterTransform),\n",
    "         ROW_AXIS(timecode(times)),\n",
    "         SERIES_ID(idcols),\n",
    "         PAYLOAD(FIELDS(Weekly_Sales),CONTENT(REAL))\n",
    "     ),\n",
    "     FUNC_PARAMS(\n",
    "         TIMECODE(\n",
    "              START_VALUE(TIMESTAMP '2010-02-05 00:00:00'), \n",
    "              DURATION(WEEKS(1))\n",
    "          ),\n",
    "          INTERPOLATE(LINEAR)\n",
    "     )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ts_sf_resampled_ads_tw')\n",
    "    execute_sql(query)\n",
    "\n",
    "# Create a DataFrame from a query\n",
    "DataFrame.from_query('SELECT TOP 10 * FROM ts_sf_resampled_ads_tw').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df7eb95e-5834-4daa-a456-45541932e3cc",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Resampling converts irregular time-series into regular time series i.e. makes the interval same throughout the time series.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51ff9a26",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>4. Data Exploration</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd96fdcf-35d8-4dfe-bb8b-2fc54ddcc8fc",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_UnivariateStatistics</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Displays descriptive statistics for each specified numeric input table column.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Exploration-Functions/TD_UnivariateStatistics/TD_UnivariateStatistics-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Exploration-Functions/TD_UnivariateStatistics/TD_UnivariateStatistics-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2fc7ed-e4e4-4b7c-b2de-1a0a876bd6f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "SELECT * \n",
    "FROM TD_UnivariateStatistics (\n",
    "    ON az_sf_joined AS InputTable\n",
    "    USING\n",
    "    TargetColumns ('Size','Weekly_Sales','Fuel_Price','Unemployment','Temperature','CPI')\n",
    "    Stats( \n",
    "              'MEAN',\n",
    "              'TRIMMED MEAN',\n",
    "              'MEDIAN',\n",
    "              'MODE',           \n",
    "              'SKEWNESS',\n",
    "              'STANDARD ERROR',\n",
    "              'STANDARD DEVIATION',\n",
    "              'KURTOSIS',\n",
    "              'SUM',\n",
    "              'RANGE',\n",
    "              'VARIANCE',\n",
    "              'INTERQUARTILE RANGE',\n",
    "              'MINIMUM',\n",
    "              'MAXIMUM')\n",
    "    \n",
    ") As dt;\n",
    "\n",
    "'''\n",
    "# Create a DataFrame from a query\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b736afd-86fb-418d-8d37-57a5d7ad11f0",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows univariate statistics of each of the numeric columns.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "879f6f7d-2545-49d7-8d7a-c8b5c9b58a52",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_CategoricalSummary</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Displays the distinct values and their counts for each specified input table.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Target_column</b>: Column for which to display distinct values and their counts.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>ColumnName</b>: Name of target_column.</li>\n",
    "    <li><b>DistinctValue</b>: Name of distinct value in target_column. Table has one row for each distinct value.</li>\n",
    "    <li><b>DistinctValueCount</b>: Count of distinct value in target_column. Table has one row for each distinct value.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Exploration-Functions/TD_CategoricalSummary/TD_CategoricalSummary-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a53e3956-c390-4a47-a1e3-cc3321458483",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "SELECT * FROM TD_CategoricalSummary (\n",
    "    ON az_sf_joined AS InputTable\n",
    "    USING\n",
    "        TargetColumns ('Store','Dept') \n",
    ") AS dt;\n",
    "'''\n",
    "\n",
    "# Create a DataFrame from a query\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaeb6d5d-6ce6-4215-b5cb-a8c02e61e326",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows the count of weekly sales for each Department ID.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "134fee17-d329-4462-80b4-48b83de435cc",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ColumnSummary</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Displays the following for each specified input table column:</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Column name</li>\n",
    "    <li>Data type</li>\n",
    "    <li>Count of these values (Non-Null, Null, Blank, Zero, Positive, Negative)</li>\n",
    "    <li>Percentage of these values (Null and Non Null)</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Target_column</b>: Column for which to display summary.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Analytics-Database-Analytic-Functions-17.20/Data-Exploration-Functions/TD_ColumnSummary/TD_ColumnSummary-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a42af00",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_ColumnSummary function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c539175-2d37-41c6-839d-58546a36b16c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "SELECT * FROM TD_ColumnSummary (\n",
    "    ON az_sf_joined AS InputTable\n",
    "    USING\n",
    "    TargetColumns ('Size','Weekly_Sales','Fuel_Price','Unemployment','Temperature','CPI','Dept')\n",
    ") AS dt;\n",
    "'''\n",
    "\n",
    "# Create a DataFrame from a query\n",
    "DataFrame.from_query(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a08d3bf9-d88d-4f45-931a-9264076b63cc",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows column wise summary.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d81f06c-03e6-416e-983d-84cd3a627873",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Plot for Exploratory Data Analysis</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_Plot provides the ability to generate charts. The generated charts can be in the JPG, PNG, or SVG formats. The following chart styles are available:</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Line Plot <span style='color: green;'>(used in this demo)</span></li>\n",
    "    <li>Scatter Plot <span style='color: green;'>(used in this demo)</span></li>\n",
    "    <li>Bar Plot <span style='color: green;'>(used in this demo)</span></li>\n",
    "    <li>Mesh Plot <span style='color: orange;'>(not relevant for this demo)</span></li>\n",
    "    <li>Seismic Wiggle Plot <span style='color: orange;'>(not relevant for this demo)</span></li>\n",
    "    <li>Geometry Plot <span style='color: orange;'>(not relevant for this demo)</span></li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/General-Utility-Functions/TD_PLOT/TD_PLOT-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li><b>Derived-series identifier</b>: The resultant series identifier inherited from SERIES_ID or MATRIX_ID declaration.</li>\n",
    "        <li><b>IMAGE</b>: Plot image in the format specified in the function command.</li>\n",
    "</ul>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de252d69",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table in which there are weekly sales against unique store which we will be using in later visualization functions</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8213305a-26fa-492c-aed2-a545e1c19379",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE weekly_sales_p_store AS ( \n",
    "    SELECT 1 AS id, CAST(Store as BYTEINT) as Store, AVG(Weekly_Sales) AS Weekly_Sales \n",
    "    FROM az_sf_joined\n",
    "    GROUP BY Store\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('weekly_sales_p_store')\n",
    "    execute_sql(query)\n",
    "\n",
    "# Create a DataFrame from a query\n",
    "DataFrame.from_query('SELECT TOP 10 * FROM weekly_sales_p_store')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70837701-bed4-4c75-b0f8-15e8b597a5c3",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows store-wise weekly sales.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a6b6c42-5fa2-473e-bc09-bee9bf04f297",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Scatter Plot</b></li>\n",
    "    <ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>The scatter plot in this demo demonstrates the relation between stores and weekly sales which helps in finding the relation between them.</li>\n",
    "        <li>Visualizes which store is running more sales as compared to others.</li>\n",
    "        <li>On the Y-Axis you will find the unique store IDs.</li>\n",
    "        <li>On the X-Axis you will find the weekly sales against each store.</li>\n",
    "        <li>The plot is visualized horizontally for better visualization experience.</li>\n",
    "    </ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cfbae466",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47850d3d-840c-49c2-ab1b-35cca2d57466",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(scatter_graph_results)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(weekly_sales_p_store),\n",
    "        ROW_AXIS(SEQUENCE(Weekly_Sales)),\n",
    "        SERIES_ID(id),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Store),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(1,1),\n",
    "        TITLE('SCATTER GRAPH'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TITLE ('DATASET TIME SERIES'),\n",
    "                TYPE('scatter'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('WEEKLY_SALES'),\n",
    "                YLABEL('STORES')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('scatter_graph_results')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "294e8f3e",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code creates a scatter plot using TD_PLOT.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table which will store the results for the scatter plot built using TD_Plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf28d206-0b74-4349-b7f7-dcb3c794d0cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE scatter_plot AS (\n",
    "    SELECT * FROM scatter_graph_results\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('scatter_plot')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e5ca50d",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8e0a070",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:18:14.860692Z",
     "start_time": "2022-12-15T17:18:11.043297Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"scatter_plot\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1d3d6bf-cea0-4b23-b850-5b642f8ea8f5",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The graph here shows the weekly sales of stores. We do not see any pattern here.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3fd9bfc-b5fb-44eb-86cd-a962948d3b81",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Bar Chart</b></li>\n",
    "    <ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>The Bar chart demonstrates the relation between weekly sales per unique stores.</li>\n",
    "        <li>A raise in bar shows the increase in weekly sales while a low highted bar signifies lower number of weekly sales.</li>\n",
    "        <li>On the X-Axis you will find unique store IDs.</li>\n",
    "        <li>On the Y-Axis you will find weekly sales against each store.</li>\n",
    "        <li>The plot is visualized vertically for better visualization experience.</li>\n",
    "    </ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "404983dd",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4013ab54-2bc2-44ca-a660-1ef43a589580",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(bar_graph_results)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(weekly_sales_p_store),\n",
    "        ROW_AXIS(SEQUENCE(Store)),\n",
    "        SERIES_ID(id),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Weekly_Sales),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(1,1),\n",
    "        TITLE('BAR GRAPH'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TITLE ('DATASET TIME SERIES'),\n",
    "                TYPE('bar'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('STORES'),\n",
    "                YLABEL('WEEKLY_SALES')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('bar_graph_results')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf39a35b",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code creates a bar plot using TD_PLOT.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27dd8c25-cfe0-45c4-895f-59426c3e011a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE bar_graph AS (\n",
    "    SELECT * FROM bar_graph_results\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('bar_graph')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d06032d6",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef805911",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:18:55.994706Z",
     "start_time": "2022-12-15T17:18:52.485370Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"bar_graph\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d26db00-a88a-4e37-a466-145a92f137f9",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above plot shows weekly sales per Store ID. This can help us to see which stores make high weekly sales.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "536c7681-ce46-42a9-a649-e3c802e4b49e",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Line Chart</b></li>\n",
    "    <ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>The line chart in this demo demonstrates the relation between time_series and weekly sales.</li>\n",
    "        <li>Illustrates the trend between time series and weekly sales.</li>\n",
    "        <li>Also shows the general trend of the number of sales over a certain period of time.</li>\n",
    "        <li>X-Axis shows time series for a specific Department, Store ID.</li>\n",
    "        <li>Y-Axis shows the weekly sales against each store.</li>\n",
    "    </ul>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c085d180",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29492f75-ce9a-4818-a431-cbe22af74343",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(line_graph_results)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Weekly_Sales),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    )WHERE idcols='40-41',\n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(1,1),\n",
    "        TITLE('LINE GRAPH'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TITLE ('DATASET TIME SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('TIMESERIES'),\n",
    "                YLABEL('WEEKLY_SALES')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('line_graph_results')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcbb4d70",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code creates a line plot using TD_PLOT.</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table which will store the results for the Line plot build using TD_Plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cfcb1a4-ff99-4c53-b81b-78e7a9e5b8f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE line_graph AS (\n",
    "    SELECT * FROM line_graph_results\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('line_graph')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7451ce1d",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1534d36e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:19:22.979095Z",
     "start_time": "2022-12-15T17:19:17.808102Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"line_graph\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4555066e-0444-40bd-9984-36a32a322c90",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above graph shows us the trend os weekly sales with time. We can infer that the sales are increasing with time.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d63fdf00-f04e-4a90-9e79-0e4b80b87cde",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_PowerSpec</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Converts a series from the time or spatial domain to the frequency domain in order to facilitate frequency domain analysis. Its calculations serve to estimate the correct power spectrum associated with the series.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Temporal-and-Spatial-Functions/TD_POWERSPEC/TD_POWERSPEC-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Derived-series-identifier</b>: The resultant series identifier inherited from the SERIES_ID.</li>\n",
    "    <li><b>ROW_I</b>: The index of time series.</li>\n",
    "    <li><b>SPECTRAL_DENSITY_field</b>: The calculated magnitude.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f75f319c-9ad4-452c-9f9c-5391f43295ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(PWRSPEC_RES_tw)\n",
    "TD_POWERSPEC( \n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw), \n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        FREQ_STYLE(\"K_RADIANS\")\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PWRSPEC_RES_tw')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "df083be7-26be-4a4b-87c3-91ba30439d28",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame.from_query('SELECT TOP 10 * FROM PWRSPEC_RES_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f41840b7-3aeb-415e-a584-243403ce34f5",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows the weekly sales in frequency domain. The time series has been converted from time domain to frequency domain.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ceb7e94b-5183-4f2c-b974-7eca81e60924",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ACF</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Calculates the autocorrelation or autocovariance of a time series. Autocorrelation and autocovariance show how the time series correlates or covaries with itself when delayed by a lag in time or space. When TD_ACF is computed, a coefficient corresponding to a particular lag is affected by all the previous lags.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ACF/TD_ACF-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ACF/TD_ACF-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e398862",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_ACF function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d13d4e8-b96a-416e-9b74-85f862242504",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION COLUMNS(OUT_Weekly_Sales AS Weekly_Sales) INTO ART(ACF_RES_tw)\n",
    "TD_ACF ( \n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        MAXLAGS(14),\n",
    "        FUNC_TYPE(0),\n",
    "        QSTAT(1),\n",
    "        DEMEAN(1),\n",
    "        ALPHA(0.05)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ACF_RES_tw')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b87ea2a5-4305-4fbd-8a26-d290369174ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame.from_query('SELECT TOP 10 * FROM ACF_RES_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5662dfe1-73ef-4280-8266-db23e3378acc",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows the result of TD_ACF function i.e. the Auto-Correlation of the time series. This will be used to decide whether the series is stationary or not.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0e402c8-7008-45c4-94c0-c3e82aeeabad",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_PACF</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Provides insight as to whether the function being modeled is stationary or not. The partial auto correlations are used to measure the degree of correlation between time series sample points. The algorithm removes the effects of the previous lag.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_PACF/TC_PACF-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_PACF/TD_PACF-Output'>refer here</a></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fafe3dc6",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_PACF function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f99d67a8-70f1-4bee-951b-0d0da5720104",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION COLUMNS(OUT_Weekly_Sales AS Weekly_Sales) INTO ART(PACF_RES_tw)\n",
    "\n",
    "TD_PACF (\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        MAXLAGS(14),\n",
    "        UNBIASED(0),\n",
    "        INPUT_TYPE(DATA_SERIES),\n",
    "        ALGORITHM(LEVINSON_DURBIN),\n",
    "        ALPHA(0.05)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PACF_RES_tw')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f33d59c-e178-4216-9104-0496615016a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "DataFrame.from_query('SELECT TOP 10 * FROM PACF_RES_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0195dc88-388c-4708-b8d8-5cd1839c4404",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above output shows the result of TD_PACF function i.e. the Partial Auto-Correlation of the time series. This will be used to decide whether the series is stationary or not.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9dee06-3a60-403c-afc3-f95a06733f97",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Plot for stationarity panel</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>TD_Plot for stationary panel to visualize ACF, PACF, and Line plot.</li>\n",
    "    <li>Panel will display all consolidated plots.</li>\n",
    "    <li>The panel visualizes the results and shows the values of ARIMA model parameters in terms of p and q.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e79224ca",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa02d486-807b-4dd6-adb2-b56d6f1dd632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(stationarity_pannel_result)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(Weekly_Sales),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(ACF_RES_tw),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD (FIELDS(Weekly_Sales, CONF_OFF_Weekly_Sales),CONTENT(MULTIVAR_REAL) )\n",
    "    ) WHERE idcols='40-41',\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(PACF_RES_tw),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD (FIELDS(Weekly_Sales, CONF_OFF_Weekly_Sales),CONTENT(MULTIVAR_REAL) )\n",
    "    ) WHERE idcols='40-41',\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(PWRSPEC_RES_tw),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(SPECTRAL_DENSITY_Weekly_Sales),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    )WHERE idcols='40-41',\n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        LAYOUT(2,2),\n",
    "        WIDTH(1920),\n",
    "        HEIGHT(1080),\n",
    "        TITLE('STATIONARITY PANEL'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                ID(1),\n",
    "                CELL(1,1),\n",
    "                TITLE ('DATASET TIME SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('X SeqNo'),\n",
    "                YLABEL('Y Magnitude')\n",
    "            ),\n",
    "            (   ID(2),\n",
    "                CELL(1,2),\n",
    "                TITLE ('DATASET ACF RESULT SERIES'),\n",
    "                TYPE('corr')\n",
    "               ,XLABEL('X LAG')\n",
    "               ,YLABEL('Y Magnitude')\n",
    "            ),\n",
    "            (   ID(3),\n",
    "                CELL(2,1),\n",
    "                TITLE ('DATASET PACF SERIES'),\n",
    "                TYPE('corr')\n",
    "               ,XLABEL('X LAG')\n",
    "               ,YLABEL('Y Magnitude')\n",
    "            ),\n",
    "            (\n",
    "                ID(4),\n",
    "                CELL(2,2),\n",
    "                TYPE('line')\n",
    "               ,MARKER('o')\n",
    "               ,XLABEL('X ROW_I in W K_RADIANS')\n",
    "              ,YLABEL('Y SPECTRAL DENSITY MAGNITUDE')\n",
    "              ,TITLE ('DATASET POWER SPECTRUM RESULTS')\n",
    "              ,LEGEND('best')\n",
    "              ,GRID(FORMAT('-'))\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('stationarity_pannel_result')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "755a50ab",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table which will store the results for the scatter plot build using TD_Plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "059d659d-da69-4791-b895-f1abcbe6ec21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE stationarity_pannel_image AS (\n",
    "    SELECT * FROM stationarity_pannel_result\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('stationarity_pannel_image')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ce667a1",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e3e76a4",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:20:29.447947Z",
     "start_time": "2022-12-15T17:20:21.415043Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"stationarity_pannel_image\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546b34e9-b27c-43c0-86d0-6f2ee32e8a0c",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The first plot shows the weekly sales w.r.t time. The second plat shows the Auto-Correlation. The third plot shows the Partial Auto-Correlation. The fourth part shows the plot of Spectral Density i.e. the time series converted from time domain to frequency domain.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c6667f8-c37e-411b-9a7c-ee7485de4ff4",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Checking for Stationarity</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Why we are checking stationarity?</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Our model expects that the input to the model is independent of the trend, seasonality, and noise, so we have to convert non-stationary data to stationary to make the series model ready.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>How do we identify non-stationary series?</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>TD_DickyFuller()</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>How we remove the non-stationarity or seasonality?</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>TD_Diff()</li>\n",
    "    <li>TD_SeasonalNormalize()</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7303a19",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing rows from the resampled dataset for a specific depart-store id (idcols)</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee551bca-e107-4abd-b892-6a9a1722b892",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = \"\"\"\n",
    "    SELECT *\n",
    "    FROM ts_sf_resampled_ads_tw\n",
    "    WHERE idcols = '40-41';\n",
    "\"\"\"\n",
    "\n",
    "DataFrame.from_query(query).sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cb3ec95-3432-4724-bece-5d9275232d5c",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_DickeyFuller</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Tests for the presence of one or more unit roots in a series to determine if the series is non-stationary. A unit root test tests whether a time series is not stationary and consists of a unit root in time series analysis. The presence of a unit root in time series defines the null hypothesis, and the alternative hypothesis defines time series as stationary.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Mathematically the unit root test can be represented as:</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    $y_t=D_t+z_t+\\varepsilon_t$\n",
    "    <br>\n",
    "    Where,\n",
    "</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>$D_t$ is the deterministic component.</li>\n",
    "    <li>$\\mathrm{Z}_{{t}}$ is the stochastic component.</li>\n",
    "    <li>$\\varepsilon_t$ is the stationary error process.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Dickey Fuller Test:</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li><b>Null Hypothesis:</b> The series is non-stationary</li>\n",
    "    <li><b>Accept:</b> greater than 0.05 (fail to reject null-hypothesis) - non-stationary</li>\n",
    "    <li><b>Reject:</b> less than 0.05 (Reject null hypothesis) - Stationary</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_DICKEY_FULLER/TD_DICKEY_FULLER-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_DICKEY_FULLER/TD_DICKEY_FULLER-Output'>refer here</a></p>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16b9ee69",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the Dickey Fuller test and building the table for the results</li>\n",
    "    <li>Printing the results from the Dickey Fuller test</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4df76a37-de35-4469-af50-9d6a1b6c6ad6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO VOLATILE ART(DF_RES_tw)\n",
    "TD_DICKEY_FULLER(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        ALGORITHM('NONE')\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('DF_RES_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('DF_RES_tw')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a977a2e-b3b9-4fb5-abe4-044eed1efab2",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows that NULL_HYP is accepted which means that the series is not stationary.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc076514-cd37-4b5a-bd93-88d997230ea2",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Diff</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Transforms stationary, seasonal, or non-stationary time series into differenced time series.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_DIFF/TD_DIFF-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_DIFF/TD_DIFF-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f28a5013",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the difference function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5839f896-0cd5-40e0-9842-36e54b95b555",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(DIFF_Series)\n",
    "TD_DIFF(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        DIFFERENCES(1),\n",
    "        LAG(1),\n",
    "        SEASONAL_MULTIPLIER(0)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('DIFF_Series')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT TOP 10 * from DIFF_Series;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29ba63f3-49ee-46d5-8493-3b03a532f711",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Differenceing is one of the ways to make series stationary.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Once the time series is converted to differenced time series using TD_Diff, we verify the presence of non-stationary using TD_DickyFuller test.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7b821e-79c5-4beb-ba3e-0697a57880e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(DF_RES_tw)\n",
    "TD_DICKEY_FULLER(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(DIFF_Series),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(OUT_Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        ALGORITHM('NONE')\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('DF_RES_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('DF_RES_tw')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2805bb3c-c3fc-40ab-aec8-0a4069c02ad0",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>As the above output shows that the NULL_HYP is Rejected which means that by differencing the series, we are able to make the time series stationary</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>This shows that the time series is now stationary. </li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3235abac-a87e-4fed-8396-d5dd45fee33b",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_SeasonalNormalize</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_SeasonalNormalize takes a non-stationary series and normalizes the series by removing unit roots.</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The function can be used with any cyclic data that can be subdivided into a collection of logical periods, in which each period can be further subdivided into a collection of logical intervals.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_SEASONALNORMALIZE/TD_SEASONALNORMALIZE-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_SEASONALNORMALIZE/TD_SEASONALNORMALIZE-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24386464",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_SeasonalNormalize function and building the table for the results</li>\n",
    "    <li>Printing top 10 rows from the SN_Series table to analyze the change in the dataset using the TD_SeasonalNormalize() function</li>\n",
    "    <li>Also removing the seasonality using TD_SeasonalNormalize and confirming using TD_DickeyFuller if the time series is stationary</li>\n",
    "    <li>Extracting the metadata from the seasonal normalize function</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aa2cd79-0973-4ebc-af83-6def888623d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO VOLATILE ART(SN_Series)\n",
    "TD_SEASONALNORMALIZE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(Weekly_Sales),\n",
    "            CONTENT(REAL)\n",
    "        ),\n",
    "        INTERVAL(DAYS(7))\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        SEASON_CYCLE(CYCLES(\"WEEKS\"), DURATION(4))\n",
    "    ),\n",
    "    OUTPUT_FMT(INDEX_STYLE(NUMERICAL_SEQUENCE))\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SN_Series')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT TOP 10 * from SN_Series;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12977374-ac60-4fa6-9902-14f39c2be208",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The output above shows the normalized series which is done by removing the unit roots.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29abff7e-ca5f-49a4-b85c-2f241a4489c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(NORM_METADATA)\n",
    "TD_EXTRACT_RESULTS(\n",
    "   ART_SPEC(\n",
    "       TABLE_NAME(SN_Series),\n",
    "       LAYER(ARTMETADATA)\n",
    "   )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('NORM_METADATA')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT TOP 10 * from NORM_METADATA;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73242be0",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>If the mean and variance is somewhat similar then we consider that seasonality has been removed from the series. Remember that Variance is the square of Standard Deviation. Here we do not see that the seasonality is removed.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1115c963",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>5. Building Model</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f76a25-caae-494e-afc8-692b35fc35b2",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Base Model</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>TD_ARIMAEstimate for model estimation</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>ARIMA is a statistical model for analyzing and forecasting time series data. It adds the parts of integration to regression for making the data stationary with the use of differentiation. ARIMA is a notation for (p,d,q) where the parameters are filled with values to indicate the ARIMA model being used for the purpose of training, testing, and forecasting.</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>ARIMA consists of the following parameters:</p>\n",
    "<ol style='font-size:16px;font-family:Arial;;color:#00233C'>\n",
    "    <li>p: Lag or previous value count to predict the forecast.</li>\n",
    "    <li>d: Number of differentiation for making the data stationary.</li>\n",
    "    <li>q: Moving average window size.</li>\n",
    "</ol>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>ARMA (p, q) Model:</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>$Y_t=\\beta_o+\\beta_1 Y_{t-1}+\\ldots+\\beta_p Y_{t-p}+\\epsilon_t+\\theta_1 \\epsilon_{t-1}+\\ldots+\\theta_q \\epsilon_{t-q}$</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>- The differentiation (if any) must be reversed to obtain forecast:</p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>if $\\mathrm{d}=0: \\quad Y_t^{\\prime}=y_t^{\\prime}$</li>\n",
    "    <li>if $\\mathrm{d}=1: \\quad Y_t^c=y_t^{\\prime}+Y_{t-1}$</li>\n",
    "    <li>if $\\mathrm{d}=2: \\quad Y_t^c=y_y^{\\prime}+2 Y_{t-1}-Y_{t-2}$</li>\n",
    "</ul>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>TD_ARIMAEstimate estimates the coefficients corresponding to an ARIMA (AutoRegressive Integrated Moving Average) model and fits a series with an existing ARIMA model. The function can also provide the \"goodness of fit\" and the residuals of the fitting operation. The function generates a model layer used as input for the TD_ARIMAValidate and TD_ARIMAForecast functions. This function is for univariate series.</li>\n",
    "    <li>TD_ARIMAEstimate takes one or two inputs, and the second input is optional. The first input is a time series, and the second input references the model context. When only one input is passed in, TD_ARIMAEstimate operates in a coefficient estimate mode. When two inputs are passed in, TD_ARIMAEstimate operates in a model apply mode. When the second input is passed in, you must include an INPUT_FMT(MODE()) clause.</li>\n",
    "    <li>You can use the FIT_PERCENTAGE parameter to pass a portion of the data, such as 80%, to the TD_ARIMAEstimate function. The ART produced includes the ARTVALDATA layer, which contains the remaining 20%, and can be used with TD_ARIMAValidate for the validation exercise.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAESTIMATE/TD_ARIMAESTIMATE-Syntax-Elements'>refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href='https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAESTIMATE/TD_ARIMAESTIMATE-Output'>refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11f80432",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_ARIMAEstimate function and building the table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34dde789-8534-470c-876c-ae6e1a229f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION \n",
    "INTO ART(SAV_ARMA_1_1L2_tw_base)\n",
    "\n",
    "TD_ARIMAESTIMATE(\n",
    "    SERIES_SPEC(TABLE_NAME(DIFF_Series), SERIES_ID(idcols), ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "    PAYLOAD(FIELDS(OUT_Weekly_Sales), CONTENT(REAL))) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        NONSEASONAL(MODEL_ORDER(3,1,1)), \n",
    "        LAGS(MA(1)), \n",
    "        CONSTANT(1), \n",
    "        ALGORITHM(MLE), \n",
    "        FIT_PERCENTAGE(70),\n",
    "        FIT_METRICS(1), \n",
    "        COEFF_STATS(1), \n",
    "        RESIDUALS(1), \n",
    "        MAX_ITERATIONS(100)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARMA_1_1L2_tw_base;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d685e3-4e43-49bf-84e5-ac25506d5ace",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code trains ARIMA model and saves the results in an ART. This trained model will be further used to validate and forecast the weekly sales.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d31d834-76d1-4b2f-988f-d5c0a8ead59d",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The auxiliary layers are as follows:</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>ARTFITRESIDUALS contains the residual series.</li>\n",
    "    <li>ARTFITMETADATA contains the goodness-of-fit metrics.</li>\n",
    "    <li>ARTMODEL shows the validation model context.</li>\n",
    "    <li>ARTVALDATA is used for the internal validation process.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_EXTRACT_RESULTS function retrieves the meta-data of the training model we trained in the above cell and shows the known error estimates like Mean Error, Mean Average Error, Mean Squared Error, etc.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea67643a",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_ExtractResults function and building the table for results</li>\n",
    "    <li>Printing the extract result to check for errors like Mean Error, Mean Average Error, etc</li>\n",
    "    <li>Printing the extract result output to check for errors like Mean Error, Mean Average Error, etc</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa4eb90e-b497-4416-b4e9-f7c0001fdd4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_GOF_ARMA_1_1L2_tw_base)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_1_1L2_tw_base),\n",
    "        LAYER(ARTFITMETADATA)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_GOF_ARMA_1_1L2_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_GOF_ARMA_1_1L2_tw_base')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c49565-6cf7-4e4a-ab07-8b417b16e169",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The metadata for the trained ARIMA mode is shown above. This lets us know how well our model has trained on the training data.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the TD_ExtractResults table holding the data for residuals</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f799ec8f-6fe1-4f1d-9b7a-7b968988b74c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(ARMA_1_1L2_RESIDUALS_tw_base)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_1_1L2_tw_base),\n",
    "        LAYER(ARTFITRESIDUALS)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ARMA_1_1L2_RESIDUALS_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from ARMA_1_1L2_RESIDUALS_tw_base;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7fe0d6c-b60d-4152-96f5-3fb7c060d2dd",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The residual can be seen from the above result. This is the error that the model does while predicting the weekly sales. The lesser this value, the better the model.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating table in which there are actual and predicted values from the model and assigning them unique ids to differentiate them</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38eef002-82a7-4f2f-917c-42b75831a73b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE PLOT_ARIMA2_FIT_base (\n",
    "    idcols VARCHAR(10),\n",
    "    ROW_I BIGINT,\n",
    "    FIT_MAGNITUDE FLOAT\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PLOT_ARIMA2_FIT_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_FIT_base\n",
    "SELECT '40-411', ROW_I, ACTUAL_VALUE\n",
    "FROM ARMA_1_1L2_RESIDUALS_tw_base\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_FIT_base\n",
    "SELECT '40-412', ROW_I, CALC_VALUE\n",
    "FROM ARMA_1_1L2_RESIDUALS_tw_base\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "543be904-9ee9-497a-9bf1-c9ed5f36b480",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT TOP 10 *\n",
    "    FROM PLOT_ARIMA2_FIT_base;\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query).sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba970d28-9071-4bbb-bb15-ada5604bc628",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above table would be used in TD_PLOT function to generate a plot.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75538a79-ab2a-4d6d-82e8-d227dae3bc98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(ts_sf_estimated_series_plot_base)\n",
    "TD_Plot(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(PLOT_ARIMA2_FIT_base),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD(\n",
    "           FIELDS(FIT_MAGNITUDE),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        WIDTH(1920),\n",
    "        HEIGHT(1080),\n",
    "        TITLE('ARIMA ESTIMATE PHASE: ACTUAL 40-411 and FITTED 40-412 : SALES FORECAST'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                TITLE ('ARIMA ESTIMATED: ORIGINAL(40-411 ) and ESTIMATED(40-412) SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                SERIES[\n",
    "                    (\n",
    "                        ID(1),\n",
    "                        FORMAT('r--')\n",
    "                    ),\n",
    "                    (\n",
    "                        ID(2),\n",
    "                        FORMAT('b-')\n",
    "                    )\n",
    "                ],\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('X SeqNo'),\n",
    "                YLABEL('Y Magnitude')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ts_sf_estimated_series_plot_base')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655a1179",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table which will store the results for the ts_sf_estimated_series_plot build using TD_Plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76f4e6ea-2d08-4f91-8814-b614a51c0bcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE ts_sf_estimated_series_plot_img_base AS (\n",
    "SELECT *\n",
    "FROM ts_sf_estimated_series_plot_base\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ts_sf_estimated_series_plot_img_base')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91f1c52a",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "312f73ec",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:24:00.182298Z",
     "start_time": "2022-12-15T17:23:49.587754Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"ts_sf_estimated_series_plot_img_base\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526bc5cb-0de8-437c-83d7-bc8ecb7ca050",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The graph above shows the original series (in Red) and the estimated series(in Blue). The model is able to estimate fairly well as seen from the above graph.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4944c71-a707-4ef1-9d54-396411d14359",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Checking serial correlation in estimated data residuals</b></p>\n",
    "\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_DurbinWatson</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><strong>Description</strong></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Determines serial correlation between residuals within an independent time series table</li>\n",
    "    <li>The Durbin Watson (DW) statistic is a test for autocorrelation in the residuals from a statistical model or regression analysis</li>\n",
    "    <li>The Durbin-Watson statistic will always have a value ranging between 0 and 4</li>\n",
    "    <li>A value of 2.0 indicates there is no autocorrelation detected in the sample</li>\n",
    "    <li>Values from 0 to less than 2 point to positive autocorrelation and values from 2 to 4 means negative autocorrelation</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><strong>Special Considerations:</strong></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>A rule of thumb is that DW test statistic values in the range of 1.5 to 2.5 are relatively normal. Values outside this range could, however, be a cause for concern.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><strong>Function Elements</strong></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_DURBIN_WATSON/TD_DURBIN_WATSON-Syntax-Elements\">refer here</a></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><strong>Function Output</strong></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_DURBIN_WATSON/TD_DURBIN_WATSON-Output\">refer here</a></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "779fd53a-3cb0-4ab2-8825-6b57c3c81410",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_DurbinWatson function and building the table for results</li>\n",
    "    <li>Printing the SAV_ARMA_1_1L2_DW_tw table and checking the results according to thresholds defined in the function.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fde0af1-0bac-421a-a206-ee8dfcce4826",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_1_1L2_DW_tw_base)\n",
    "TD_DURBIN_WATSON(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ARMA_1_1L2_RESIDUALS_tw_base),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(RESIDUAL),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        EXPLANATORY_COUNT(1),\n",
    "        INCLUDE_CONSTANT(1),\n",
    "        METHOD(DW_FORMULA),\n",
    "        SIGNIFICANCE_LEVEL(.05)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_DW_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_1_1L2_DW_tw_base')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "883d8269-add6-4715-946c-75aefe338ba8",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The result above shows the DW_Value approximately 2. Which indicates that there is no autocorrelation detected in the sample.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47e57ce3-a737-4f04-bcad-5f7cc7faeff9",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_BreushGodfrey</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <b>Description:</b><br>\n",
    "    Checks for the presence of serial correlation among the residual and error terms after running a regression associated with a fitted model. With respect to regression models, it is expected that there is no serial correlation among the error terms.<br>\n",
    "    Whereas the Durbin-Watson Test is restricted to detecting first-order autoregression, the Breusch-Godfrey (BG) Test can detect autocorrelation up to any predesignated order p.<br>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <b>p-value:</b><br>\n",
    "    <ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "        <li>less than 0.05 autocorrelation exists</li>\n",
    "        <li>greater than 0.05 autocorrelation does not exist</li>\n",
    "    </ul>\n",
    "</p>\n",
    "\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <b>Function Elements:</b><br>\n",
    "    For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_BREUSCH_GODFREY/TD_BREUSCH_GODFREY-Syntax-Elements\">refer here</a><br>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <b>Function Output:</b><br>\n",
    "    For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Diagnostic-Statistical-Test-Functions/TD_BREUSCH_GODFREY/TD_BREUSCH_GODFREY-Output\">refer here</a><br>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "135f3d47",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_BreushGodfrey function and building the table for the results</li>\n",
    "    <li>Printing the SAV_ARMA_1_1L2_BG_tw table and checking the results according to the thresholds defined in the description of the function.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e0f6442-fddf-4cad-93ac-ba4e3114b82a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_1_1L2_BG_tw_base)\n",
    "TD_BREUSCH_GODFREY(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ARMA_1_1L2_RESIDUALS_tw_base),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(ROW_I, residual),\n",
    "            CONTENT(MULTIVAR_REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        EXPLANATORY_COUNT(2),\n",
    "        RESIDUAL_MAXLAGS(4),\n",
    "        SIGNIFICANCE_LEVEL(.01)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_BG_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_1_1L2_BG_tw_base')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b4bcc2e-3f1a-4e7f-92cf-efe968a98151",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The P_Value here is greater than 0.05 and hence we can say that there is no autocorrelation between residual and error terms.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dffb193",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>6. Validating model</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "949af7fc-66f2-4846-9830-13f0644ab104",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ARIMAValidate for model validation</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_ARIMAValidate provides data scientists with a m, such that a number of model validation tests can be performed.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>It performs in-sample forecasting for both seasonal and non-seasonal auto-regressive (AR), moving-average (MA), and ARIMA models. It also supports the extended Box-Jenkins seasonal ARIMA model formula.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAVALIDATE/TD_ARIMAVALIDATE-Syntax-Elements\">refer here</a>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAVALIDATE/TD_ARIMAVALIDATE-Output\">refer here</a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a8cb145",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_ARIMAValidate function and building the table for the results</li>\n",
    "    <li>Printing the SAV_ARMA_VALIDATE_1_1L2_tw table to check how our model performed on the validation dataset. In the table, we can check the AIC, Mean Squared Error, and other parameters to assess the model's performance.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e6598d7-94e2-46e6-b36a-886be626ba95",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_VALIDATE_1_1L2_tw_base)\n",
    "TD_ARIMAVALIDATE(\n",
    "    ART_SPEC(TABLE_NAME(SAV_ARMA_1_1L2_tw_base)),\n",
    "    FUNC_PARAMS(\n",
    "        FIT_METRICS(1),\n",
    "        RESIDUALS(1)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_VALIDATE_1_1L2_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_VALIDATE_1_1L2_tw_base')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1502e737-3606-4c0c-992e-1d10202ec8ed",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above result shows the performance metrics of the trained ARIMA model. These help us determine how well our model performs on unseen data.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cb7a92c-a930-454e-86f6-b4c5ad71a696",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ExtractResults for retrieving residuals</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>ARTFITRESIDUALS parameter shows how much the predicted value is deviated from the actual value. The difference is also known as residuals.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Printing the results of the SAV_ARMA_VALIDATE_RESIDUALS_tw table.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1215fae3-098c-492c-a623-9156f55b7abc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO VOLATILE ART(SAV_ARMA_VALIDATE_RESIDUALS_tw_base)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(TABLE_NAME(SAV_ARMA_VALIDATE_1_1L2_tw_base), LAYER(ARTFITRESIDUALS))\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_VALIDATE_RESIDUALS_tw_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARMA_VALIDATE_RESIDUALS_tw_base;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5410d096-926c-44c2-83d4-13cfdbae5c0d",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The residual can be seen from the above result. This is the error that the model does while predicting the weekly sales. The lesser this value, the better the model.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a83da61e-2da9-487b-a522-c06c81c46cbb",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Plot for plotting actual and forecasted values</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table in which there are actual and forecasted values from the model and assigning them unique IDs to differentiate them</li>\n",
    "    <li>Printing the table to validate the changes that are made in the table</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f563fde-dbd1-4846-a87b-63790c258626",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE PLOT_ARIMA2_VALIDATE_FIT_base (\n",
    "    idcols VARCHAR(10),\n",
    "    ROW_I BIGINT,\n",
    "    FIT_MAGNITUDE FLOAT\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PLOT_ARIMA2_VALIDATE_FIT_base')\n",
    "    execute_sql(query)\n",
    "    \n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_VALIDATE_FIT_base\n",
    "SELECT '40-411', ROW_I, ACTUAL_VALUE\n",
    "FROM SAV_ARMA_VALIDATE_RESIDUALS_tw_base\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_VALIDATE_FIT_base\n",
    "SELECT '40-412', ROW_I, CALC_VALUE\n",
    "FROM SAV_ARMA_VALIDATE_RESIDUALS_tw_base\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "DataFrame.from_query('SELECT * from PLOT_ARIMA2_VALIDATE_FIT_base;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb2ef45",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "843bfd20-eef0-4699-a919-f3479fe6bb52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(tf_sf_validated_series_plot_base)\n",
    "TD_Plot(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(PLOT_ARIMA2_VALIDATE_FIT_base),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD(\n",
    "           FIELDS(FIT_MAGNITUDE),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        WIDTH(1920),\n",
    "        HEIGHT(1080),\n",
    "        TITLE('ARIMA VALIDATE PHASE:  ACTUAL 40-411 and COMPUTED 40-412 : SALES FORECAST'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                TITLE ('ARIMA VALIDATE : ORIGINAL(40-411) and COMPUTED(40-412) SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                SERIES[\n",
    "                    (\n",
    "                        ID(1),\n",
    "                        FORMAT('r--')\n",
    "                    ),\n",
    "                    (\n",
    "                        ID(2),\n",
    "                        FORMAT('b-')\n",
    "                    )\n",
    "                ],\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('X SeqNo'),\n",
    "                YLABEL('Y Magnitude')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('tf_sf_validated_series_plot_base')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8875c45",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table which will store the results for the tf_sf_validated_series_plot build using TD_Plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2effc48a-c209-4079-bb54-c274b1bd5a02",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE check_image_tf_sf_validated_series_plot_base AS (\n",
    "SELECT *\n",
    "FROM tf_sf_validated_series_plot_base\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('check_image_tf_sf_validated_series_plot_base')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "452db9b1",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98dc9cce",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:25:28.162077Z",
     "start_time": "2022-12-15T17:25:23.400826Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"check_image_tf_sf_validated_series_plot_base\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6e73744-b758-4a82-8641-28f6b42d82e0",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above graph shows the Original Series (in Red) and the Computed Series (in Blue). The original series is an unseen data for our model. We can say that out model is able to decently estimate the weekly sales on unseen data.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb4240e5",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>7. Final Model</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "929a99c5",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>The final model follows the same steps as our base model with the only difference of using Cumulative smoothing on the re-sampled table.</li>\n",
    "    <li>We observe a significant improvement in terms of the difference between actual and calculated values when plotting time series ahead.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dced5518-6f53-4f10-b490-9891e0c0519b",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_SmoothMA for Cumulative Smoothing</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Applies a smoothing function to a time series which results in a series that highlights the time series mean. For non-stationary time series with non-constant means, the smoothing function is used to create a result series.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>When the result series is subtracted from the original series, it removes the non-stationary mean behavior.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>You can use the new time series to build an ARIMA forecasting model.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_SMOOTHMA/TD_SMOOTHMA-Syntax-Elements\">refer here</a>.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_SMOOTHMA/TD_SMOOTHMA-Output\">refer here</a>.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1b8931c-f6c9-44f7-b443-7ff4bbfebc26",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(Ex_Smoothing)\n",
    "TD_SMOOTHMA(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ts_sf_resampled_ads_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(TIMECODE(ROW_I)),\n",
    "        PAYLOAD(FIELDS(Weekly_Sales), CONTENT(REAL))\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        MA(CUMULATIVE)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('Ex_Smoothing')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from Ex_Smoothing;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4835550-ac8d-413c-8417-cc0fb82d5749",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above result shows a smoothened series using TD_SMOOTHMA. This could potentially produce a stationary series.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b186600-2fc5-40a4-8279-8068d51f2ca8",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ARIMAEstimate for model estimation</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Estimates the coefficients corresponding to an ARIMA model and fits a series with an existing ARIMA model. The function can also provide the \"goodness of fit\" and the residuals of the fitting operation.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The function generates a model layer used as input for the TD_ARIMAValidate and TD_ARIMAForecast functions. This function is for univariate series.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75aa5f18-2d89-4f2f-8b49-ac9fa743c26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO VOLATILE ART(SAV_ARMA_1_1L2_tw)\n",
    "TD_ARIMAESTIMATE(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(Ex_Smoothing),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(FIELDS(Weekly_Sales), CONTENT(REAL))\n",
    "    ) WHERE idcols='40-41',\n",
    "    FUNC_PARAMS(\n",
    "        NONSEASONAL(MODEL_ORDER(3,1,1)),\n",
    "        LAGS(MA(1)),\n",
    "        CONSTANT(1),\n",
    "        ALGORITHM(MLE),\n",
    "        FIT_PERCENTAGE(70),\n",
    "        FIT_METRICS(1),\n",
    "        COEFF_STATS(1),\n",
    "        RESIDUALS(1),\n",
    "        MAX_ITERATIONS(100)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARMA_1_1L2_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11dba825-3480-4c34-afc8-b9933c639a03",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code trains ARIMA model and saves the results in an ART. This trained model will be further used to validate and forecast the weekly sales.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfe95c52-e908-41df-9d02-04441761cd64",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ExtractResults for retrieving residuals</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Retrieves auxiliary result sets stored in an ART.</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>The auxiliary layers are as follows:</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>ARTFITRESIDUALS contains the residual series.</li>\n",
    "    <li>ARTFITMETADATA contains the goodness-of-fit metrics.</li>\n",
    "    <li>ARTMODEL shows the validation model context.</li>\n",
    "    <li>ARTVALDATA is used for the internal validation process.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    This function retrieves the meta-data of the training model we trained in the above cell and shows the known error estimates like Mean Error, Mean Average Error, Mean Squared Error, etc.\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26e813f6-539e-40fe-b6f6-38f5d12ec320",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_GOF_ARMA_1_1L2_tw)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_1_1L2_tw),\n",
    "        LAYER(ARTFITMETADATA)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_GOF_ARMA_1_1L2_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_GOF_ARMA_1_1L2_tw;')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3f91cbb-47e6-4690-a4de-14586eddeb9c",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The metadata for the trained ARIMA mode is shown above. This lets us know how well our model has trained on the training data.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the TD_ExtractResults table holding the data for residuals</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "492d4d75-d875-48ca-bc3e-47d591461576",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(ARMA_1_1L2_RESIDUALS_tw)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_1_1L2_tw),\n",
    "        LAYER(ARTFITRESIDUALS)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ARMA_1_1L2_RESIDUALS_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from ARMA_1_1L2_RESIDUALS_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bbc8ff3-3b7b-4036-92d0-71b60c7cdddb",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The residual can be seen from the above result. This is the error that the model does while predicting the weekly sales. The lesser this value, the better the model.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b471cf4-4ac0-487c-95ee-690126e294cc",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Plot for plotting actual and calculated value</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the top 10 rows in the ARMA_1_1L2_RESIDUALS_tw table</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adbb96c9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:25:59.811580Z",
     "start_time": "2022-12-15T17:25:58.151841Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "query = '''\n",
    "    SELECT *\n",
    "    FROM ARMA_1_1L2_RESIDUALS_tw;\n",
    "'''\n",
    "\n",
    "DataFrame.from_query(query).sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8e169a",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table in which there are actual and predicted values from the model and assigning them unique IDs to differentiate them</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24179a7f-e9bc-408d-a485-49b01c356aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE PLOT_ARIMA2_FIT (\n",
    "    idcols VARCHAR(10),\n",
    "    ROW_I BIGINT,\n",
    "    FIT_MAGNITUDE FLOAT\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PLOT_ARIMA2_FIT')\n",
    "    execute_sql(query)\n",
    "    \n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_FIT\n",
    "SELECT '40-411', ROW_I, ACTUAL_VALUE\n",
    "FROM ARMA_1_1L2_RESIDUALS_tw\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_FIT\n",
    "SELECT '40-412', ROW_I, CALC_VALUE\n",
    "FROM ARMA_1_1L2_RESIDUALS_tw\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "DataFrame.from_query('SELECT * from PLOT_ARIMA2_FIT;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6de34cf8",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "868978e4-9e6c-48cd-a48a-651b7d380227",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(ts_sf_estimated_series_plot)\n",
    "TD_Plot(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(PLOT_ARIMA2_FIT),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD(\n",
    "            FIELDS(FIT_MAGNITUDE),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        WIDTH(1920),\n",
    "        HEIGHT(1080),\n",
    "        TITLE('ARIMA ESTIMATE PHASE: ACTUAL 40-411 and FITTED 40-412 : SALES FORECAST'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                TITLE('ARIMA ESTIMATED: ORIGINAL(40-411 ) and ESTIMATED(40-412) SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                SERIES[\n",
    "                    (\n",
    "                        ID(1),\n",
    "                        FORMAT('r--')\n",
    "                    ),\n",
    "                    (\n",
    "                        ID(2),\n",
    "                        FORMAT('b-')\n",
    "                    )\n",
    "                ],\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('X SeqNo'),\n",
    "                YLABEL('Y Magnitude')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ts_sf_estimated_series_plot')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from ARMA_1_1L2_RESIDUALS_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b1f0b77-5982-4d7c-afbd-f1b21025858f",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The residual can be seen from the above result. This is the error that the model does while predicting the weekly sales. The lesser this value, the better the model.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f8a9cc6-f330-4253-a6f9-11db9b7200cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE ts_sf_estimated_series_plot_img AS (\n",
    "SELECT *\n",
    "FROM ts_sf_estimated_series_plot\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('ts_sf_estimated_series_plot_img')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f1b9587-51d2-4b54-be23-195bbb7bd218",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table to store the results for the ts_sf_estimated_series_plot built using TD_Plot</li>\n",
    "    <li>Converting the data from the Teradata table to a Pandas DataFrame to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a117ea6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:26:48.227704Z",
     "start_time": "2022-12-15T17:26:43.983186Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"ts_sf_estimated_series_plot_img\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f568339-223b-45a1-8173-a69d4d6d3d1c",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The graph above shows the original series (in Red) and the estimated series(in Blue). The model is able to estimate excellently as seen from the above graph.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1334c9db",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>Checking serial correlation in estimated data residuals</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4edef941-7a9d-4aad-90e7-13c9d984e386",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_DurbinWatson</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Determines serial correlation between residuals within an independent time series table</li>\n",
    "    <li>The Durbin Watson (DW) statistic is a test for autocorrelation in the residuals from a statistical model or regression analysis.</li>\n",
    "    <li>The Durbin-Watson statistic will always have a value ranging between 0 and 4.</li>\n",
    "    <li>A value of 2.0 indicates there is no autocorrelation detected in the sample.</li>\n",
    "    <li>Values from 0 to less than 2 point to positive autocorrelation, and values from 2 to 4 mean negative autocorrelation.</li>\n",
    "</ul>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Special Considerations:</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>A rule of thumb is that DW test statistic values in the range of 1.5 to 2.5 are relatively normal. Values outside this range could, however, be a cause for concern.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f6d26ab-e740-4018-a94a-5d1e043ab298",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_1_1L2_DW_tw)\n",
    "TD_DURBIN_WATSON(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ARMA_1_1L2_RESIDUALS_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(RESIDUAL),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        EXPLANATORY_COUNT(1),\n",
    "        INCLUDE_CONSTANT(1),\n",
    "        METHOD(DW_FORMULA),\n",
    "        SIGNIFICANCE_LEVEL(.05)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_DW_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_1_1L2_DW_tw')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2906690-26e2-4676-8819-83fdbb19f6a3",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The result above shows the DW_Value approximately 2. Which indicates that there is no autocorrelation detected in the sample.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d93171ae-2d6a-47a3-b5f7-45b78cbd6a01",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_BreuschGodfrey</b></p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Checks for the presence of serial correlation among the residual and error terms after running a regression associated with a fitted model.</li>\n",
    "    <li>With respect to regression models, it is expected that there is no serial correlation among the error terms.</li>\n",
    "    <li>Whereas the Durbin-Watson Test is restricted to detecting first-order autoregression, the Breusch-Godfrey (BG) Test can detect autocorrelation up to any predesignated order p.</li>\n",
    "</ul>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>p-value:</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>less than 0.05 autocorrelation exists</li>\n",
    "    <li>greater than 0.05 autocorrelation does not exist</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b723379b-7124-45aa-900c-9129f853a148",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the SAV_ARMA_1_1L2_BG_tw table and checking the results according to the thresholds defined in the description of the function.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d31fcb3c-4c73-4ede-8dcf-7384d5927879",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_1_1L2_BG_tw)\n",
    "TD_BREUSCH_GODFREY(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(ARMA_1_1L2_RESIDUALS_tw),\n",
    "        SERIES_ID(idcols),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(\n",
    "            FIELDS(ROW_I, residual),\n",
    "            CONTENT(MULTIVAR_REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        EXPLANATORY_COUNT(2),\n",
    "        RESIDUAL_MAXLAGS(4),\n",
    "        SIGNIFICANCE_LEVEL(.01)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_1_1L2_BG_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_1_1L2_BG_tw')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03854eee-9cd8-48f7-8233-4ae550d67fcc",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The P_Value here is significantly greater than 0.05 indicating that there is no autocorrelation between residual and error terms.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c00039",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>8. Validating model</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "179f545b-056c-487b-a1e8-91035d259978",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ARIMAValidate for model validation</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>TD_ARIMAValidate provides data scientists with a metrics collection for model selection and the produced residuals, such that a number of model validation tests can be performed.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>It performs in-sample forecasting for both seasonal and non-seasonal auto-regressive (AR), moving-average (MA), and ARIMA models. It also supports the extended Box-Jenkins seasonal ARIMA model formula.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>A validation dataset is a sample of data held back from training your model that is used to give an estimate of model skill while tuning model’s hyperparameters. We used 70% of the data in training and held back 30% of the data for the validation set. It can vary from use case to use case.\n",
    "</p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAVALIDATE/TD_ARIMAVALIDATE-Syntax-Elements\">refer here</a>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Model-Preparation-and-Parameter-Estimation-Functions/TD_ARIMAVALIDATE/TD_ARIMAVALIDATE-Output\">refer here</a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0aaec23-6f8b-4165-aad8-ff74ec283ed5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_VALIDATE_1_1L2_tw)\n",
    "TD_ARIMAVALIDATE(\n",
    "    ART_SPEC(TABLE_NAME(SAV_ARMA_1_1L2_tw)),\n",
    "    FUNC_PARAMS(\n",
    "        FIT_METRICS(1),\n",
    "        RESIDUALS(1)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_VALIDATE_1_1L2_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame('SAV_ARMA_VALIDATE_1_1L2_tw')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de64f103-cb39-4fe6-a5e1-c0fd90a65fed",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above result shows the performance metrics of the trained ARIMA model. These help us determine how well our model performs on unseen data.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0348d9",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ExtractResults for retrieving residuals</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99266b45-a153-4ef7-b722-f7a03df3e657",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_VALIDATE_RESIDUALS_tw)\n",
    "TD_EXTRACT_RESULTS(\n",
    "    ART_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_VALIDATE_1_1L2_tw),\n",
    "        LAYER(ARTFITRESIDUALS)\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_VALIDATE_RESIDUALS_tw')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARMA_VALIDATE_RESIDUALS_tw;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fce8231-3cef-44b1-9971-08c8729a1663",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The residual can be seen from the above result. This is the error that the model does while predicting the weekly sales. The lesser this value, the better the model.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d08b1b66-40fe-407d-9874-e1190256a2d2",
   "metadata": {},
   "source": [
    "<hr style=\"height:1px;border:none;background-color:#00233C;\">\n",
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_Plot for plotting actual and forecasted values</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Creating a table in which there are actual and predicted values from the model and assigning them unique IDs to differentiate them.</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af24294d-9d19-4870-93b8-b4c32e98e04c",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE PLOT_ARIMA2_VALIDATE_FIT (\n",
    "    idcols VARCHAR(10),\n",
    "    ROW_I BIGINT,\n",
    "    FIT_MAGNITUDE FLOAT\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('PLOT_ARIMA2_VALIDATE_FIT')\n",
    "    execute_sql(query)\n",
    "    \n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_VALIDATE_FIT\n",
    "SELECT '40-411', ROW_I, ACTUAL_VALUE\n",
    "FROM SAV_ARMA_VALIDATE_RESIDUALS_tw\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "execute_sql('''\n",
    "INSERT INTO PLOT_ARIMA2_VALIDATE_FIT\n",
    "SELECT '40-412', ROW_I, CALC_VALUE\n",
    "FROM SAV_ARMA_VALIDATE_RESIDUALS_tw\n",
    "WHERE idcols = '40-41' AND ROW_I > 0;\n",
    "''')\n",
    "\n",
    "DataFrame.from_query('SELECT * from PLOT_ARIMA2_VALIDATE_FIT;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a761c73-68a7-4299-bbf2-2e43e3d70c35",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(tf_sf_validated_series_plot)\n",
    "TD_Plot\n",
    "(\n",
    "    SERIES_SPEC\n",
    "    (\n",
    "        TABLE_NAME(PLOT_ARIMA2_VALIDATE_FIT),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD\n",
    "        (\n",
    "           FIELDS(FIT_MAGNITUDE),\n",
    "           CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS\n",
    "    (\n",
    "        WIDTH(1920),\n",
    "        HEIGHT(1080),\n",
    "        TITLE('ARIMA VALIDATE PHASE: ACTUAL 40-411 and COMPUTED 40-412 : SALES FORECAST'),\n",
    "        PLOTS[\n",
    "            (\n",
    "                TITLE ('ARIMA VALIDATE: ORIGINAL(40-411) and COMPUTED(40-412) SERIES'),\n",
    "                GRID(FORMAT('-')),\n",
    "                TYPE('line'),\n",
    "                SERIES[\n",
    "                    (\n",
    "                        ID(1),\n",
    "                        FORMAT('r--')\n",
    "                    ),\n",
    "                    (\n",
    "                        ID(2),\n",
    "                        FORMAT('b-')\n",
    "                    )\n",
    "                ],\n",
    "                MARKER('o'),\n",
    "                LEGEND('best'),\n",
    "                XLABEL('X SeqNo'),\n",
    "                YLABEL('Y Magnitude')\n",
    "            )\n",
    "        ]\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('tf_sf_validated_series_plot')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "755925a5-83e1-450f-8cfa-ff3b9915ec4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE check_image_tf_sf_validated_series_plot AS (\n",
    "    SELECT * FROM tf_sf_validated_series_plot\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('check_image_tf_sf_validated_series_plot')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7937c853-6049-4726-acb9-33b5658dd689",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Creating a table to store the results for the ts_sf_estimated_series_plot built using TD_Plot</li>\n",
    "    <li>Converting the data from the Teradata table to a Pandas DataFrame to visualize the plot</li>\n",
    "    <li>After the ARIMA model validation is completed, we plot the actual and predicted time series and can see a significant improvement in the closeness between the two.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3416245b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:28:02.939161Z",
     "start_time": "2022-12-15T17:27:53.799356Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"check_image_tf_sf_validated_series_plot\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc93de31-63fa-4180-98fe-37ea7497b833",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above graph shows the Original Series (in Red) and the Computed Series (in Blue). The original series is an unseen data for our model. We can say that out model is able to excellently estimate the weekly sales on unseen data.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "240d6407",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>9. Testing Validation Residuals</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4a3ccca-f23d-4384-8cac-275687b4e885",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_LineSpec</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Identifies periodicity that may be inherent in an input series.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Temporal-and-Spatial-Functions/TD_LINESPEC/TD_LINESPEC-Syntax-Elements\">refer here</a>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Temporal-and-Spatial-Functions/TD_LINESPEC/TD_LINESPEC-Output\">refer here</a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "523fac4f-76e3-453e-a72d-328f3ec967fb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARIMA2_RESID_LINESPEC)\n",
    "TD_LINESPEC(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(SAV_ARMA_VALIDATE_RESIDUALS_tw), \n",
    "        SERIES_ID(idcols), \n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        PAYLOAD(FIELDS(RESIDUAL), CONTENT(REAL))\n",
    "    ),\n",
    "    FUNC_PARAMS(FREQ_STYLE(\"K_PERIODICITY\"))\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARIMA2_RESID_LINESPEC')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARIMA2_RESID_LINESPEC;').sort('ROW_I')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40da1336-c693-46f6-83e4-124a02a0991d",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above code looks for periodicity in the input series.</p>\n",
    "\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Printing the results in the SAV_ARIMA2_RESID_LINESPEC table</li>\n",
    "    <li>Applying the TD_Plot function and building an ART table for the results</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a5f683-75b5-4aa8-b0ad-346a1386df46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(tf_sf_line_spectrum_plot)\n",
    "TD_PLOT(\n",
    "    SERIES_SPEC(\n",
    "        TABLE_NAME(SAV_ARIMA2_RESID_LINESPEC),\n",
    "        ROW_AXIS(SEQUENCE(ROW_I)),\n",
    "        SERIES_ID(idcols),\n",
    "        PAYLOAD(\n",
    "            FIELDS(SPECTRAL_DENSITY_RESIDUAL),\n",
    "            CONTENT(REAL)\n",
    "        )\n",
    "    ),\n",
    "    FUNC_PARAMS(\n",
    "        TITLE('Line Spectrum on Validate Residual Sales ARIMA2'),\n",
    "        PLOTS[(\n",
    "            SERIES[(ID(1), FORMAT('-o'))],\n",
    "            TYPE('line')\n",
    "        )],\n",
    "        IMAGE('png')\n",
    "    )\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('tf_sf_line_spectrum_plot')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c44021c2-ce53-4b74-8978-2efbea2ca903",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "CREATE MULTISET TABLE check_image_tf_sf_line_spectrum_plot AS (\n",
    "    SELECT * FROM tf_sf_line_spectrum_plot\n",
    ") WITH DATA;\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('check_image_tf_sf_line_spectrum_plot')\n",
    "    execute_sql(query)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6ce8afa",
   "metadata": {},
   "source": [
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Converting the data from teradata table to pandas dataframe to visualize the plot</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64b4b1be",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-15T17:29:07.719318Z",
     "start_time": "2022-12-15T17:29:02.222584Z"
    }
   },
   "outputs": [],
   "source": [
    "plot_df = DataFrame(\"check_image_tf_sf_line_spectrum_plot\").to_pandas()\n",
    "\n",
    "img = plot_df.IMAGE.iloc[0]\n",
    "Image.open(io.BytesIO(img))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38278ee-a71b-4b06-8396-0f77b5a07085",
   "metadata": {},
   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The above graph is a Line Spectrum the validate the Residual sales. The graph shows that the residuals are fairly low and the model is performing well.</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f323dc88",
   "metadata": {},
   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>10. Forecasting values</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08cb9b87-6ba9-4991-9ee1-21a0d4b3bfaf",
   "metadata": {},
   "source": [
    "<p style='font-size:18px;font-family:Arial;color:#00233C'><b>TD_ARIMAForecast</b></p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Description</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Forecast a user-defined number of periods based on models fitted from the TD_ARIMAEstimate function.\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Elements</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Series-Forecasting-Functions/TD_ARIMAFORECAST/TD_ARIMAFORECAST-Syntax-Elements\">refer here</a>\n",
    "</p>\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'><b>Function Output</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>For further reading, <a href=\"https://docs.teradata.com/r/Teradata-VantageTM-Unbounded-Array-Framework-Time-Series-Reference/Series-Forecasting-Functions/TD_ARIMAFORECAST/TD_ARIMAFORECAST-Output\">refer here</a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44044dff-7007-4cc2-bdd9-66f932171fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the SQL query\n",
    "query = f'''\n",
    "EXECUTE FUNCTION INTO ART(SAV_ARMA_FORECAST_1_1L2_tf)\n",
    "TD_ARIMAFORECAST(\n",
    "    ART_SPEC(TABLE_NAME(SAV_ARMA_VALIDATE_1_1L2_tw)),\n",
    "    FUNC_PARAMS(FORECAST_PERIODS(7))\n",
    ");\n",
    "'''\n",
    "\n",
    "# Execute the query and handle exceptions\n",
    "try:\n",
    "    execute_sql(query)\n",
    "except:\n",
    "    # Drop the table if an exception occurs\n",
    "    db_drop_table('SAV_ARMA_FORECAST_1_1L2_tf')\n",
    "    execute_sql(query)\n",
    "    \n",
    "DataFrame.from_query('SELECT * from SAV_ARMA_FORECAST_1_1L2_tf;').sort('ROW_I')"
   ]
  },
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   "source": [
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>We finally forecast the amount of future weekly sales for 7 Periods i.e. 7 weeks.</p>\n",
    "    \n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Businesses can better plan for future action items and learn from historical data.</li>\n",
    "    <li>As a result, predict how their company will be performing in terms of future revenue and make much-informed decisions.</li>\n",
    "    <li>Forecasting can be applied in many industries including Retail, Operations, Marketing, Manufacturing, Logistics, or any industry that deals with uncertainties in the global and domestic market.</li>\n",
    "</ul>"
   ]
  },
  {
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   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>11. Cleanup</b>"
   ]
  },
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    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Work Tables</b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>Cleanup work tables to prevent errors next time.</p>"
   ]
  },
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    "tables = [\n",
    "    \"outlier_fit\", \"az_sf_joined\", \"OutlierFilterTransform\", \"ts_sf_resampled_ads_tw\",\n",
    "    \"weekly_sales_p_store\", \"scatter_graph_results\", \"scatter_plot\", \"bar_graph_results\",\n",
    "    \"bar_graph\", \"line_graph_results\", \"line_graph\", \"PWRSPEC_RES_tw\", \"ACF_RES_tw\",\n",
    "    \"PACF_RES_tw\", \"stationarity_pannel_result\", \"stationarity_pannel_image\", \"DF_RES_tw\",\n",
    "    \"DIFF_Series\", \"DF_RES_tw\", \"SN_Series\", \"NORM_METADATA\", \"SAV_ARMA_1_1L2_tw_base\",\n",
    "    \"SAV_GOF_ARMA_1_1L2_tw_base\", \"ARMA_1_1L2_RESIDUALS_tw_base\", \"PLOT_ARIMA2_FIT_base\",\n",
    "    \"ts_sf_estimated_series_plot_base\", \"ts_sf_estimated_series_plot_img_base\",\n",
    "    \"SAV_ARMA_1_1L2_DW_tw_base\", \"SAV_ARMA_1_1L2_BG_tw_base\", \"SAV_ARMA_VALIDATE_1_1L2_tw_base\",\n",
    "    \"SAV_ARMA_VALIDATE_RESIDUALS_tw_base\", \"PLOT_ARIMA2_VALIDATE_FIT_base\", \"tf_sf_validated_series_plot_base\",\n",
    "    \"check_image_tf_sf_validated_series_plot_base\", \"Ex_Smoothing\", \"SAV_ARMA_1_1L2_tw\",\n",
    "    \"SAV_GOF_ARMA_1_1L2_tw\", \"ARMA_1_1L2_RESIDUALS_tw\", \"PLOT_ARIMA2_FIT\", \"ts_sf_estimated_series_plot\",\n",
    "    \"ts_sf_estimated_series_plot_img\", \"SAV_ARMA_1_1L2_DW_tw\", \"SAV_ARMA_1_1L2_BG_tw\",\n",
    "    \"SAV_ARMA_VALIDATE_1_1L2_tw\", \"SAV_ARMA_VALIDATE_RESIDUALS_tw\", \"PLOT_ARIMA2_VALIDATE_FIT\",\n",
    "    \"tf_sf_validated_series_plot\", \"check_image_tf_sf_validated_series_plot\", \"SAV_ARIMA2_RESID_LINESPEC\",\n",
    "    \"tf_sf_line_spectrum_plot\", \"check_image_tf_sf_line_spectrum_plot\", \"SAV_ARMA_FORECAST_1_1L2_tf\"\n",
    "]\n",
    "\n",
    "\n",
    "for table in tables:\n",
    "    try:\n",
    "        db_drop_table(table_name = table)\n",
    "    except:\n",
    "        pass"
   ]
  },
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    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'> <b>Databases and Tables </b></p>\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>The following code will clean up tables and databases created above.</p>"
   ]
  },
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    "%run -i ../run_procedure.py \"call remove_data('DEMO_SalesForecastingUAF');\"        # Takes 10 seconds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "remove_context()"
   ]
  },
  {
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   "source": [
    "<hr style=\"height:2px;border:none;background-color:#00233C;\">\n",
    "<b style = 'font-size:20px;font-family:Arial;color:#00233C'>Dataset:</b>\n",
    "\n",
    "- `Store`: Store number\n",
    "- `Date`: Week\n",
    "- `Temperature`: Average temperature in the region\n",
    "- `Fuel_Price`: Cost of fuel in the region\n",
    "- `MarkDown1`: Anonymized data related to promotional markdowns that Walmart is running.\n",
    "- `MarkDown2`: Anonymized data related to promotional markdowns that Walmart is running.\n",
    "- `MarkDown3`: Anonymized data related to promotional markdowns that Walmart is running.\n",
    "- `MarkDown4`: Anonymized data related to promotional markdowns that Walmart is running.\n",
    "- `MarkDown5`: Anonymized data related to promotional markdowns that Walmart is running.\n",
    "- `CPI`: The consumer price index\n",
    "- `Unemployment`: The unemployment rate\n",
    "- `IsHoliday`: Whether the week is a special holiday week\n",
    "- `Type`: Store type has been provided, there are 3 types — A, B and C.\n",
    "- `Size`: Stores size has provided\n",
    "- `Dept`: The department number\n",
    "- `Weekly_Sales`: Sales for the given department in the given store\n",
    "\n",
    "<p style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    Dataset source: <a href=\"https://www.kaggle.com/datasets/aslanahmedov/walmart-sales-forecast?select=stores.csv\">Kaggle</a>\n",
    "</p>"
   ]
  },
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "<p style = 'font-size:18px;font-family:Arial;color:#00233C'><b>Links:</b></p>\n",
    "<ul style = 'font-size:16px;font-family:Arial;color:#00233C'>\n",
    "    <li>Teradataml Python reference: <a href = 'https://docs.teradata.com/search/all?query=Python+Package+User+Guide&content-lang=en-US'>here</a></li>\n",
    "    <li><a href='https://docs.teradata.com/search/all?query=Database+Analytic+Functions&content-lang=en-US'>Advanced SQL Engine 17.20 Functions</a></li>\n",
    "<li><a href='https://docs.teradata.com/search/all?query=Database+Unbounded+Array+Framework+Time+Series+Functions&content-lang=en-US'>UAF Time-Series 17.20 Functions</a></li>\n",
    "</ul>"
   ]
  },
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    "<footer style=\"padding-bottom:35px; background:#f9f9f9; border-bottom:3px solid #00233C\">\n",
    "    <div style=\"float:left;margin-top:14px\">ClearScape Analytics™</div>\n",
    "    <div style=\"float:right;\">\n",
    "        <div style=\"float:left; margin-top:14px\">\n",
    "            Copyright © Teradata Corporation - 2023. All Rights Reserved\n",
    "        </div>\n",
    "    </div>\n",
    "</footer>"
   ]
  }
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