Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
-
Updated
Dec 18, 2018 - Python
Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data
A simple way to keep track of an Exponential Moving Average (EMA) version of your pytorch model
Implementation of Mega, the Single-head Attention with Multi-headed EMA architecture that currently holds SOTA on Long Range Arena
The collections of simple, weighted, exponential, smoothed moving averages.
Calculate an exponential moving average from an array of numbers.
Fastest Technical Indicators written in typescript, Supports: Browser, NodeJS, ES6, CommonJS. More than +100 indicators(SMA, EMA, RSI, MACD, ...)
tools for finding/selecting options using the e*trade developer API
Modified Extended Kalman Filter with generalized exponential Moving Average and dynamic Multi-Epoch update strategy (MEKF_MAME)
A python package to extract historical market data of cryptocurrencies and to calculate technical price indicators.
A simple, customizable EMA Crossover Forex trading algorithm made with Oanda's Rest v20 API.
Forecasting Time Series with Moving Average and Exponential Smoothing
Online statistics implementations, including average, variance and standard deviation; exponentially weighted versions as well.
A Stock Prices Analytics Dashboard, comprising of python codes for price predictions, technical indicators, and dashboard hosting
iOS iBeacon based indoor location application
Testing the profitability of an algo-trading algorithm which uses exponential moving averages
mic_py : Python 3 code for successful use of microphone on windows. stdev_ema.py : Python 3 code for calculation of standard deviation and exponential moving average of stock data.
This code is part of the "Comparison of K-Means and Model-Based Clustering methods for drill core pseudo-log generation based on X-Ray Fluorescence Data" written by researchers of the Directory of Geology and Mineral Resources from the Geological Survey of Brazil – CPRM.
Analyzed historical monthly sales data of a company. Created multiple forecast models for two different products of a particular Wine Estate and recommended the optimum forecasting model to predict monthly sales for the next 12 months along with appropriate lower and upper confidence limits
This project is dedicated to forecasting 1-hour EURUSD exchange rates through the strategic amalgamation of advanced deep learning techniques. The incorporation of key technical indicators—RSI, MA, EMA, and VWAP—enhances the model's grasp of market dynamics
Implementation of EMA in tensorflow2. It's very easy to use.
Add a description, image, and links to the exponential-moving-average topic page so that developers can more easily learn about it.
To associate your repository with the exponential-moving-average topic, visit your repo's landing page and select "manage topics."