Multivariate Time Series Forecasting with Causal-Temporal Attention Network

Abstract

The task of multivariate time series (MTS) forecasting has attracted much attention in recent years. However, most existing methods overlook the causal relationship among different variables, which may lead to inaccurate forecasting results. In this paper, we incorporate causality into the forecasting procedure of MTS. We first use a causal discovery algorithm to obtain the causal graph of the MTS and then design a novel Causal-Temporal Attention Mechanism to encode the causal graph and the MTS into a set of feature tensors. Finally, a linear decoder is adopted to derive the forecasting results from the feature tensors. Experiment results on five real-world datasets indicate that our method outperforms the state-of-the-art models. Moreover, extra experiments are conducted to validate the effectiveness of hyperparameters and the modules in our method.