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Time series modeling has attracted extensive research efforts; however,achieving both reliable efficiency and interpretability from a unified modelstill remains a challenging problem. Among the literature, shapelets offerinterpretable and explanatory insights in the classification tasks, while mostexisting works ignore the differing representative power at different timeslices, as well as (more importantly) the evolution pattern of shapelets. Inthis paper, we propose to extract time-aware shapelets by designing a two-leveltiming factor. Moreover, we define and construct the shapelet evolution graph,which captures how shapelets evolve over time and can be incorporated into thetime series embeddings by graph embedding algorithms. To validate whether therepresentations obtained in this way can be applied effectively in variousscenarios, we conduct experiments based on three public time series datasets,and two real-world datasets from different domains. Experimental resultsclearly show the improvements achieved by our approach compared with 17state-of-the-art baselines.

Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets