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，简单线性模型在长时序列预测任务中可超越多种基于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），用于长时序列预测。该模型兼具线性模型简洁高效的优势，同时能够处理协变量与非线性依赖。理论上，我们证明在特定假设条件下，该模型的最简线性类比可在线性动力系统（LDS）中实现接近最优的误差率。实验表明，我们的方法在主流长时序列预测基准上可匹敌或超越现有方法，同时比最优Transformer模型快5-10倍。