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

arXiv: https://arxiv.org/abs/1907.03329 · HTML: https://mlsanity.com/paper/1907.03329/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling](https://mlsanity.com/paper/2112.08618/) (2021)

## Abstract (arXiv)

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