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International Conference on Neural Information Processing

ICONIP 2016: Neural Information Processing pp 582–589Cite as

Time Series Classification Based on Multi-codebook Important Time Subsequence Approximation Algorithm

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

Abstract

This paper proposes a multi-codebook important time subsequence approximation (MCITSA) algorithm for time series classification. MCITSA generates a codebook using important time subsequences for each class based on the difference of categories. In this way, each codebook contains the class information itself. To predict the class label of an unseen time series, MCITSA needs to compare the similarities between important time subsequences extracted from the unseen time series and codewords of each class. Experimental results on time series datasets demonstrate that MCITSA is more powerful than PVQA in classifying time series.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61373093 and 61402310, by the Natural Science Foundation of Jiangsu Province of China under Grant No. BK20140008, by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 13KJA520001, and by the Soochow Scholar Project.

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Authors and Affiliations

  1. School of Computer Science and Technology & Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Zhiwei Tao, Li Zhang, Bangjun Wang & Fanzhang Li

Authors
  1. Zhiwei Tao
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  2. Li Zhang
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  3. Bangjun Wang
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  4. Fanzhang Li
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Corresponding author

Correspondence to Li Zhang .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Tao, Z., Zhang, L., Wang, B., Li, F. (2016). Time Series Classification Based on Multi-codebook Important Time Subsequence Approximation Algorithm. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_69

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_69

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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