  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
