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+)
Tensorflow (1.12+ to 1.14)
PyTorch (0.4.1)
Zalando Research's Dilated RNN

Dataset

Please download the M4 competition dataset directly from here and put the files in the data directory.

Running the algorithm

Either use an IDE such as PyCharm or make sure to add the es_rnn folder to your PYTHON PATH before running the main.py in the es_rnn folder. You can change the configurations of the algorithm in the config.py file.

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:

@article{ReddKhinMarini,
       author = {{Redd}, Andrew and {Khin}, Kaung and {Marini}, Aldo},
        title = "{Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm}",
      journal = {arXiv e-prints},
         year = "2019",
        month = "Jul",
          eid = {arXiv:1907.03329},
        pages = {arXiv:1907.03329},
archivePrefix = {arXiv},
       eprint = {1907.03329},
 primaryClass = {cs.LG}
}