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