Time Series Classification by Modeling the Principal Shapes

  • Zhenguo Zhang
  • Yanlong Wen
  • Ying Zhang
  • Xiaojie YuanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10569)


Time series classification has been attracting significant interests with many challenging applications in the research community. In this work, we present a novel time series classification method based on the statistical information of each time series class, called Principal Shape Model (PSM), which can quickly and effectively classify the time series even if they are very long and the dataset is very large. In PSM, the time series with the same class label in the training set are gathered to extract the principal shapes which will be used to generate the classification model. For each test sample, by comparing the minimum distance between this sample and each generated model, we can predict its label. Meanwhile, through the principal shapes, we can get the intrinsic shape variation of time series of the same class. Extensive experimental results show that PSM is orders of magnitudes faster than the state-of-art time series classification methods while achieving comparable or even better classification accuracy over common used and large datasets.


Principal shapes Time series Fitting Classification 



This work was supported by the National 863 Program of China [grant numbers 2015AA015401]; Research Foundation of Ministry of Education and China Mobile [grant number MCM20150507].


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Zhenguo Zhang
    • 1
    • 2
  • Yanlong Wen
    • 1
  • Ying Zhang
    • 1
  • Xiaojie Yuan
    • 1
    Email author
  1. 1.College of Computer and Control EngineeringNankai UniversityTianjinPeople’s Republic of China
  2. 2.Department of Computer Science and TechnologyYanbian UniversityYanjiPeople’s Republic of China

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