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

Code

• github.com/damitkwr/ESRNN-GPU
  OfficialIn paperpytorch★ 0
• github.com/petercwill/temp
  pytorch★ 0
• github.com/vmm221313/ES-RNN
  pytorch★ 0
• github.com/kdgutier/esrnn_torch
  pytorch★ 0

Abstract

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

Tasks

Reproductions