Due to their prevalence, time series forecasting is crucial in multiple domains. We seek to make state-of-the-art forecasting fast, accessible, and generalizable. ES-RNN is a hybrid between classical state space forecasting models and modern RNNs that achieved a 9.4% sMAPE improvement in the M4 competition. Crucially, ES-RNN implementation requires per-time series parameters. By vectorizing the original implementation and porting the algorithm to a GPU, we achieve up to 322x training speedup depending on batch size with similar results as those reported in the original submission. Our code can be found at: https://github.com/damitkwr/ESRNN-GPU
@article{arxiv.1907.03329,
title = {Fast ES-RNN: A GPU Implementation of the ES-RNN Algorithm},
author = {Andrew Redd and Kaung Khin and Aldo Marini},
journal= {arXiv preprint arXiv:1907.03329},
year = {2019}
}