Long-Term Forecasting with TiDE: Time-Series Dense Encoder
Abstract
Recent work has shown that simple linear models can outperform several Transformer based approaches in long term time-series forecasting. Motivated by this, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model, \underline{Ti}me-series \underline{D}ense \underline{E}ncoder (TiDE), for long-term time-series forecasting that enjoys the simplicity and speed of linear models while also being able to handle covariates and non-linear dependencies. Theoretically, we prove that the simplest linear analogue of our model can achieve near optimal error rate for linear dynamical systems (LDS) under some assumptions. Empirically, we show that our method can match or outperform prior approaches on popular long-term time-series forecasting benchmarks while being 5-10x faster than the best Transformer based model.
Cite
Text
Das et al. "Long-Term Forecasting with TiDE: Time-Series Dense Encoder." Transactions on Machine Learning Research, 2023.Markdown
[Das et al. "Long-Term Forecasting with TiDE: Time-Series Dense Encoder." Transactions on Machine Learning Research, 2023.](https://mlanthology.org/tmlr/2023/das2023tmlr-longterm/)BibTeX
@article{das2023tmlr-longterm,
title = {{Long-Term Forecasting with TiDE: Time-Series Dense Encoder}},
author = {Das, Abhimanyu and Kong, Weihao and Leach, Andrew and Mathur, Shaan K and Sen, Rajat and Yu, Rose},
journal = {Transactions on Machine Learning Research},
year = {2023},
url = {https://mlanthology.org/tmlr/2023/das2023tmlr-longterm/}
}