Real-world time-series datasets are often multivariate with complex dynamics. Commonly-used high capacity architectures like recurrent- or attention-based sequential models have become popular. However, recent work demonstrates that simple univariate linear models can outperform those deep alternatives. In this paper, we investigate the capabilities of linear models for time-series forecasting and present Time-Series Mixer (TSMixer), an architecture designed by stacking multi-layer perceptrons (MLPs). TSMixer is based on mixing operations along time and feature dimensions to extract information efficiently. On popular academic benchmarks, the simple-to-implement TSMixer is comparable to specialized state-of-the-art models that leverage the inductive biases of specific benchmarks. On the challenging and large scale M5 benchmark, a real-world retail dataset, TSMixer demonstrates superior performance compared to the state-of-the-art alternatives. Our results underline the importance of efficiently utilizing cross-variate and auxiliary information for improving the performance of time series forecasting. The design paradigms utilized in TSMixer are expected to open new horizons for deep learning-based time series forecasting.

翻译：现实世界中的时间序列数据集通常具有复杂动态特性的多元结构。尽管基于循环或注意力机制的高容量序列模型已广泛应用，但近期研究表明，简单的单变量线性模型可以超越这些深度替代方案。本文深入探究了线性模型在时间序列预测中的能力，并提出了一种通过堆叠多层感知机（MLP）设计的架构——时间序列混合器（TSMixer）。该模型通过沿时间维度和特征维度执行混合操作来高效提取信息。在主流学术基准测试中，易于实现的TSMixer与利用特定基准归纳偏好的专用先进模型性能相当。而在具有挑战性的大规模真实零售数据集M5基准上，TSMixer展现出优于当前最优替代方案的性能。我们的研究结果强调了高效利用跨变量信息和辅助信息对提升时间序列预测性能的重要性。TSMixer所采用的设计范式有望为基于深度学习的时间序列预测开辟新方向。