MuPaST: Multi-Period Aware Spatio-Temporal Representation Learning for Multivariate Time Series Classification

Xianpeng Li, Ziyang Su, Yuliang Shi, Lin Cheng, Xinjun Wang, Zhiyong Chen, Hui Li

Abstract: The goal of the multivariate time series classification task is to classify future time series data based on patterns and characteristics of historical data for prediction and decision making. Recently, as the Spatio-Temporal Graph Neural Network (STGNN) architecture has been increasingly used for Spatio-Temporal modeling, it has attracted many people to apply this framework to time series analysis. However, the Spatio-Temporal architecture still has a lot of room for improvement for time series modeling, especially in solving the Temporal Covariance Shift (TCS) problem and learning a better representation for classification. Additionally, most of the TCS problems and their solutions have been formulated for univariate time series, which, if not taken into account, would substantially degrade model performance. In this paper, we introduce a novel framework called MuPaST, which leverages a Multivariate Temporal Distribution Characterization (MTDC) module to split the time series into several suitable periods. Then we use a domain learning layer to exploit the intrinsic temporal nature and intricate inter-variable relationships. Experimental results demonstrate that our method leads to an increase in performance compared to existing methods evaluated on 26 UEA benchmark datasets. Furthermore, ablation study sheds light on the unique contributions of each component within MuPaST, elucidating its effectiveness in MTSC.