Time Series Classification by Shapelet Dictionary Learning with SVM‐Based Ensemble Classifier

Time series classification is a basic and important approach for time series data mining. Nowadays, more researchers pay attention to the shape similarity method including Shapelet‐based algorithms because it can extract discriminative subsequences from time series. However, most Shapelet‐based algorithms discover Shapelets by searching candidate subsequences in training datasets, which brings two drawbacks: high computational burden and poor generalization ability. To overcome these drawbacks, this paper proposes a novel algorithm named Shapelet Dictionary Learning with SVM‐based Ensemble Classifier (SDL‐SEC). SDL‐SEC modifies the Shapelet algorithm from two aspects: Shapelet discovery method and classifier. Firstly, a Shapelet Dictionary Learning (SDL) is proposed as a novel Shapelet discovery method to generate Shapelets instead of searching them. In this way, SDL owns the advantages of lower computational cost and higher generalization ability. Then, an SVM‐based Ensemble Classifier (SEC) is developed as a novel ensemble classifier and adapted to the SDL algorithm. Different from the classic SVM that needs precise parameters tuning and appropriate features selection, SEC can avoid overfitting caused by a large number of features and parameters. Compared with the baselines on 45 datasets, the proposed SDL‐SEC algorithm achieves a competitive classification accuracy with lower computational cost.