Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain

Official code repository for the paper "Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain". Check out our paper for more details. Accompanying datasets can be found here.

Usage

We use Hydra for config management.

Run the pre-training script:

python -m pretraining.pretrain_exp backbone=BACKBONE size=SIZE ++data.dataset_name=DATASET

• where the options for BACKBONE, SIZE options can be found in conf/backbone and conf/size respectively.
• DATASET is one of azure_vm_traces_2017, borg_cluster_data_2011, or alibaba_cluster_trace_2018.
• see confg/pretrain.yaml for more details on the options.
• training logs and checkpoints will be saved in outputs/

Run the forecast script:

python -m pretraining.forecast_exp backbone=BACKBONE forecast=FORECAST size=SIZE ++data.dataset_name=DATASET

• where the options for BACKBONE, FORECAST, SIZE options can be found in conf/backbone, conf/forecast, and conf/size respectively.
• DATASET is one of azure_vm_traces_2017, borg_cluster_data_2011, or alibaba_cluster_trace_2018.
• see confg/forecast.yaml for more details on the options.
• training logs and checkpoints will be saved in outputs/

Citation

If you find the paper or the source code useful to your projects, please cite the following bibtex:

@article{woo2023pushing,
  title={Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain},
  author={Woo, Gerald and Liu, Chenghao and Kumar, Akshat and Sahoo, Doyen},
  journal={arXiv preprint arXiv:2310.05063},
  year={2023}
}