Spatiotemporal Learning With Decoupled Causal Attention for Multivariate Time Series

Abstract

In multivariate time series prediction tasks, the inter- and intra-variable relations have significant influence on prediction outcomes. In many engineering and industrial scenarios, the multivariate time series also contain a large number of subjective influencing factors, such as settings and behaviors of users. Existing learning methods neglect the interactions of these subjective factors among variables. This leads to the learning of incorrect inter-variable influences, consequently yielding inaccurate prediction results. To address this challenge, we propose a Decoupled Casal Attention Network (DECA) for multivariate time series prediction from a spatiotemporal learning perspective. multivariate time series prediction. The causality decoupling module, based on the captured causal relations among variables, disentangles the subjective factors from the objective factors. Then the objective learning module utilizes an objective causal attention to capture objective cross-variable dependencies; while the subjective learning module utilizes a subjective causal graph attention to capture subjective influences. Finally, the prediction module fuses the multi-scale features of subjective and objective factors to produce predictions. The performance is evaluated using three benchmark datasets. Results indicate that, compared to state-of-the-art methods, DECA exhibits superior accuracy in multivariate time series prediction and can be effectively used for recommendations.