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，我们提出了一种基于多层感知机（MLP）的编码器-解码器模型——时间序列稠密编码器（TiDE），该模型兼具线性模型的简洁性与速度优势，同时能够处理协变量和非线性依赖关系。理论上，我们证明了在特定假设条件下，该模型最简化的线性对应物在线性动力系统（LDS）中可实现接近最优的错误率。实验结果表明，本方法在主流长期时间序列预测基准测试中能够匹配甚至超越现有方法，且速度较最优Transformer模型快5-10倍。