For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones. To address the difficiency in multivariate models, we introduce a method to Construct Auxiliary Time Series (CATS) that functions like a 2D temporal-contextual attention mechanism, which generates Auxiliary Time Series (ATS) from Original Time Series (OTS) to effectively represent and incorporate inter-series relationships for forecasting. Key principles of ATS - continuity, sparsity, and variability - are identified and implemented through different modules. Even with a basic 2-layer MLP as core predictor, CATS achieves state-of-the-art, significantly reducing complexity and parameters compared to previous multivariate models, marking it an efficient and transferable MTSF solution.

翻译：在多元时间序列预测领域，近期深度学习应用表明，单变量模型的表现常优于多变量模型。为弥补多变量模型的不足，本文提出一种构建辅助时间序列的方法（CATS）。该方法通过类似二维时序-上下文注意力机制的运作方式，从原始时间序列生成辅助时间序列，以有效表征并融合序列间关联关系用于预测。我们识别并借助不同模块实现了辅助时间序列的三个关键特性——连续性、稀疏性与可变性。即使采用基础的双层多层感知机作为核心预测器，CATS仍能取得最先进的预测性能，同时较以往多变量模型显著降低了复杂度与参数量，标志着其成为一种高效且可迁移的多元时间序列预测解决方案。