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.

提出了一种基于多层感知器的编码器 - 解码器模型 TiDE，用于长期时间序列预测，实现了线性模型的简单和速度，同时还能处理协变量和非线性依赖关系。在理论上，我们证明了最简单的线性模型在某些假设下可以实现近似最优的误差率。在实验上，我们展示了我们的方法可以匹配或胜过以前的方法，在流行的长期时间序列预测基准测试中比最佳基于 Transformer 的模型快 5-10 倍。