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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)

Abstract

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.

Keywords

Time series Lazy learning Local similarity Shapelet Instance selection 

Notes

Acknowledgments

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).

References

  1. 1.
    Lines, J., Bagnall, A.: Time series classification with ensembles of elastic distance measures. Data Min. Knowl. Discov. 29(3), 565–592 (2015)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Nguyen, T.L., Gsponer, S., Ifrim, G.: Time series classification by sequence learning in all-subsequence space. In: 33th International Conference on Data Engineering, pp. 947–958. IEEE Press, San Diego (2017)Google Scholar
  3. 3.
    Senin, P., Malinchik, S.: SAX-VSM: interpretable time series classification using sax and vector space model. In: 13th International Conference on Data Mining, pp. 1175–1180. IEEE Press, Dallas (2013)Google Scholar
  4. 4.
    Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM Press, Paris (2009)Google Scholar
  5. 5.
    Bagnall, A., Lines, J., Bostrom, A., Large, J., Keogh, E.: The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min. Knowl. Discov. 31(3), 606–660 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet trans-formation. Data Min. Knowl. Discov. 28(4), 851–881 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Yuan, J.D., Wang, Z.H., Han, M.: A discriminative shapelets transformation for time series classification. Int. J. Pattern Recognit. Artif. Intell. 28(6), 1–28 (2014)CrossRefGoogle Scholar
  8. 8.
    Rakthanmanon, T., Keogh, E.: Fast shapelets: a scalable algorithm for discovering time series shapelets. In: 13th SIAM International Conference on Data Mining, pp. 668–676. SIAM Press, Austin (2013)Google Scholar
  9. 9.
    Karlsson, I., Papapetrou, P., Bostrom, H.: Generalized random shapelet forests. Data Min. Knowl. Discov. 30(5), 1053–1085 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Gordon, D., Hendler, D., Rokach, L.: Fast and space-efficient shapelets-based time-series classification. Intell. Data Anal. 19(5), 953–981 (2015)CrossRefGoogle Scholar
  11. 11.
    Shi, M.H., Wang, Z.H., Yuan, J.D., Liu, H.Y.: Random pairwise shapelets forest. In: 22th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 68–80. Springer Press, Melbourne (2018)CrossRefGoogle Scholar
  12. 12.
    Hou, L., Kwok, J.T., Zurada, J.M.: Efficient learning of timeseries shapelets. In: 30th AAAI Conference on Artificial Intelligence, pp. 1209–1215. AAAI Press, Phoenix (2016)Google Scholar
  13. 13.
    Bagnall, A., Lines, J., Vickers, W., Keogh, E.: The UEA & UCR Time Series Classification Repository. www.timeseriesclassification.com

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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