CATS: Enhancing Multivariate Time Series Forecasting by Constructing
  Auxiliary Time Series as Exogenous Variables
Jiecheng Lu Xu Han Yan Sun Shihao Yang
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.