CATS: 通过构建辅助时间序列作为外生变量增强多变量时间序列预测
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
AI总结：
CATS通过构建辅助时间序列作为外生变量，有效提升多变量时间序列预测的性能，实现高效且可转移的预测解决方案。
摘要：
对于多变量时间序列预测（MTSF），最近的深度学习应用显示单变量模型经常优于多变量模型。为了解决多变量模型的不足，我们引入了一种方法来构建辅助时间序列（CATS），其功能类似于二维时间上下文注意力机制，从原始时间序列（OTS）生成辅助时间序列（ATS），以有效表示并融入系列间的关系进行预测。ATS的关键原则——连续性、稀疏性和变异性——通过不同的模块得以识别和实现。即使使用基本的两层MLP作为核心预测器，CATS也实现了最先进的性能，显著减少了复杂度和参数，标志着一种高效且可转移的MTSF解决方案。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
