Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets


  • Ziqiang Cheng Zhejiang University
  • Yang Yang Zhejiang University
  • Wei Wang State Grid Huzhou Power Supply Co. Ltd.
  • Wenjie Hu Zhejiang University
  • Yueting Zhuang Zhejiang University
  • Guojie Song Peking University




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 16 state-of-the-art baselines.




How to Cite

Cheng, Z., Yang, Y., Wang, W., Hu, W., Zhuang, Y., & Song, G. (2020). Time2Graph: Revisiting Time Series Modeling with Dynamic Shapelets. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 3617-3624. https://doi.org/10.1609/aaai.v34i04.5769



AAAI Technical Track: Machine Learning