Spatiotemporal Self-Attention-Based LSTNet for Multivariate Time Series Prediction

Abstract

Multivariate time series prediction is a critical problem that is encountered in many fields, and recurrent neural network (RNN)-based approaches have been widely used to address this problem. However, traditional RNN-based approaches for predicting multivariate time series are still facing challenges, as time series are often related to each other and historical observations in real-world applications. To address this limitation, this paper proposes a spatiotemporal self-attention mechanism-based LSTNet, which is a multivariate time series forecasting model. The proposed model leverages two self-attention strategies, spatial and temporal self-attention, to focus on the most relevant information among time series. The spatial self-attention is used to discover the dependences between variables, while temporal attention is employed to capture the relationship among historical observations. Moreover, a standard deviation term is added to the objective function to track multivariate time series effectively. To evaluate the proposed method's performance, extensive experiments are conducted on multiple benchmarked datasets. The experimental results show that the proposed method outperforms several baseline methods significantly. Therefore, the proposed spatiotemporal self-attention-based LSTNet is a promising approach for predicting multivariate time series.