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Recent work has shown that simple linear models can outperform severalTransformer based approaches in long term time-series forecasting. Motivated bythis, we propose a Multi-layer Perceptron (MLP) based encoder-decoder model,Time-series Dense Encoder (TiDE), for long-term time-series forecasting thatenjoys the simplicity and speed of linear models while also being able tohandle covariates and non-linear dependencies. Theoretically, we prove that thesimplest linear analogue of our model can achieve near optimal error rate forlinear dynamical systems (LDS) under some assumptions. Empirically, we showthat our method can match or outperform prior approaches on popular long-termtime-series forecasting benchmarks while being 5-10x faster than the bestTransformer based model.

Long-term Forecasting with TiDE: Time-series Dense Encoder