  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,
Time-series Dense Encoder (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.
