oleksastepaniuk / ESRNN-GPU

PyTorch GPU implementation of the ES-RNN model for time series forecasting

Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm

A GPU-enabled version of the hybrid ES-RNN model by Slawek et al that won the M4 time-series forecasting competition by a large margin. The details of our implementation and the results are discussed in detail on this paper

Getting Started

Prerequisites

Python (3.5+)
PyTorch (0.4.1)
Zalando Research's Dilated RNN

Built With

• Python - The data science language ;)
• PyTorch - The dynamic framework for computation

Authors

• Andrew Redd - aredd-cmu
• Kaung Khin - damitkwr
• Aldo Marini - catapulta

License

This project is licensed under the MIT License - see the LICENSE file for details

Acknowledgments

• Thank you to the original author of the algorithm Smyl Slawek slaweks17 for advice and for creating this amazing algorithm
• Zalando Research zalandoresearch for their implementation of Dilated RNN

Citation

If you choose to use our implementation in your work please cite us as:

@misc{ReddKhinMarini:esrnn,
    title = {{Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm}},
    year = {2018},
    author = {Redd, Andrew and Khin, Kaung and Marini, Aldo},
    month = {12},
    url = {https://github.com/damitkwr/ESRNN-GPU}
}