Multivariate time series classification based on fusion features

Abstract

In various areas of real life, Multivariate Time Series Classification (MTSC) is widely used. It has been the focus of attention of many researchers, and a number of MTSC methods have been proposed in recent years. These methods tend to focus on the features in only a single domain. However, they have ignored the correlation and complementarity between the features in a space of multiple domains. In this paper, a novel MTSC method based on fusion features (MTSC_FF) is presented to address this problem. Firstly, MTSC_FF extracts the frequency domain features using an attention layer based on continuous wavelet transform. In parallel, MTSC_FF extracts long-range dependency features from the time domain, using a sparse self-attention layer. Simultaneously, MTSC_FF obtains spatial correlations between multivariate time series dimensions via Kendall coefficient. Then, a graph neural network is used to fuse all features. Finally, the fusion features are used to predict the classification labels by means of the fully connected layer. The experimental results obtained on the UEA datasets show that the proposed method can achieve high accuracy. In addition, the proposed method can visualize the classification-dependent features, which is an improvement in the interpretability of the results.