Learning Discriminative Virtual Sequences for Time Series Classification

Abstract

Temporal data are continuously collected in a wide range of domains. The increasing availability of such data has led to significant developments of time series analysis. Time series classification, as an essential task in time series analysis, aims to assign a set of temporal sequences to different categories. Among various approaches for time series classification, the distance metric learning based ones, such as the virtual sequence metric learning (VSML), have attracted increased attention due to their remarkable performance. In VSML, virtual sequences attract samples from different classes to facilitate time series classification. However, the existing VSML methods simply employ fixed virtual sequences, which might not be optimal for the subsequent classification tasks. To address this issue, in this paper, we propose a novel time series classification method named Discriminative Virtual Sequence Learning (DVSL). Following the unified framework of sequence metric learning, our DVSL method jointly learns a set of discriminative virtual sequences that help separate time series samples in a feature space, and optimizes the temporal alignment by dynamic time warping. Extensive experiments on 15 UCR time series datasets demonstrate the efficiency of DVSL, compared with several representative baselines.