Long Horizon Forecasting with TiDE: Time-series Dense Encoder
Abstract
We propose a simple MLP based encoder decoder architecture for long term time-series forecasting that can handle non-linear dependencies and dynamic covariates. Our method can achieve better results in several long term forecasting benchmarks while being 5-10x faster in terms of training and inference compared to the best transformer based baselines. We also show theoretically and empirically that linear models can be near optimal when the ground truth is generated from an LDS when compared to RNN's and transformers.
Research Areas
