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.

翻译：近期研究表明，简单线性模型在长期时间序列预测中可超越多种基于Transformer的方法。受此启发，我们提出一种基于多层感知机（MLP）的编码器-解码器模型——时间序列密集编码器（TiDE），用于长期时间序列预测。该模型既具备线性模型的简洁性与高效性，又能处理协变量和非线性依赖关系。理论上，我们证明在最简线性类比下，该模型能在特定假设下针对线性动态系统（LDS）实现近最优误差率。实验表明，我们的方法在主流长期时间序列预测基准上能匹配或超越现有方法，同时相比最优Transformer模型实现5-10倍的速度提升。