Leveraging subsequence-orders for univariate and multivariate time-series classification

Highly discriminative short time-series subsequences, known as shapelets, are used to classify a time-series. The existing shapelet-based methods for time-series classification assume that shapelets are independent of each other. However, they neglect temporal dependencies among pairs of shapelets, which are informative features that exist in many applications. Within this new framework, we explore a scheme to extract informative orders among shapelets by considering the time gap between two shapelets. In addition, we propose a novel model, Pairwise Shapelet-Orders Discovery, which extracts both informative shapelets and shapelet-orders and incorporates the shapelet-transformed space with shapelet-order space for time-series classification. The hypothesis of the study is that the extracted orders could increase the confidence of the prediction and further improves the classification performance. The results of extensive experiments conducted on 75 univariate and 6 multivariate real-world datasets provide evidence that the proposed model could significantly improve accuracy on average over baseline methods.