An Effective Lazy Shapelet Discovery Algorithm for Time Series Classification

  • Wei Zhang
  • Zhihai Wang
  • Jidong YuanEmail author
  • Shilei Hao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)


Shapelet is a primitive for time series classification. As a discriminative local characteristic, it has been studied widely. However, global shapelet-based models have some obvious drawbacks. First, the progress of shapelet extraction is time consuming. Second, the shapelets discovered are merely good on average for the training instances, while local features of each instance to be classified are neglected. For that, instance selection strategy is used to improve the efficiency of feature discovery, and a lazy model based on the local characteristics of each test instance is proposed. Different from the commonly used nearest neighbor models based on global similarity, our model alleviates the uncertainty of predicted class value using local similarity. Experimental results demonstrate that the proposed model is competitive to the benchmarks and can be effectively used to discover characteristics of each time series.


Time series Lazy learning Local similarity Shapelet Instance selection 



This work is supported by National Natural Science Foundation of China (No. 61672086, 61702030, 61771058), Beijing Natural Science Foundation (No. 4182052), China Postdoctoral Science Foundation (No. 2018M631328) and the Fundamental Research Funds for the Central Universities (No. 2017YJS036, 2018JBM014).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Wei Zhang
    • 1
  • Zhihai Wang
    • 1
  • Jidong Yuan
    • 1
    Email author
  • Shilei Hao
    • 1
  1. 1.School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina

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