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A 2D Transform Based Distance Function for Time Series Classification

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2018)

Abstract

Along with the arrival of Industry 4.0 era, time series classification (TSC) has attracted a lot of attention in the last decade. The high dimensionality, high feature correlation and typically high levels of noise that found in time series bring great challenges to TSC. Among TSC algorithms, the 1NN classifier has been shown as effective and difficult to beat. The core of the 1NN classifier is the distance function. The large majority of TSC have concentrated on alternative distance functions. In this paper, a two-dimensional (2D) transform based distance (2DTbD) function is proposed. There are three steps in 2DTbD. Firstly, we convert time series to 2D space by turning time series around the coordinate origin. Then we calculate distances of each dimension. Finally, we ensemble distances in 2D space to get the final time series distance. Our distance function raises the accuracy rate through the fusion of 2D information. Experimental results demonstrate that the classification accuracy can be improved by 2DTbD.

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Notes

  1. 1.

    Web page for our code: https://github.com/sdujicun/XY.

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (61872222, 91546203), the National Key Research and Development Program of China (2017YFA0700601), the Major Program of Shandong Province Natural Science Foundation (ZR2018ZB0419), the Key Research and Development Program of Shandong Province (2017CXGC0605, 2017CXGC0604, 2018GGX101019).

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Cun Ji & Xiunan Zou

  2. School of Software, Shandong University, Jinan, China

    Yupeng Hu & Shijun Liu

  3. Engineering Research Center of Digital Media Technology, Ministry of Education, Jinan, China

    Shijun Liu

  4. Shandong Provincial Key Laboratory of Software Engineering, Shandong University, Jinan, China

    Cun Ji

Authors
  1. Cun Ji
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  2. Xiunan Zou
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  3. Yupeng Hu
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  4. Shijun Liu
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Corresponding authors

Correspondence to Cun Ji , Yupeng Hu or Shijun Liu .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. University of West London, London, UK

    Xinheng Wang

  3. Hangzhou Dianzi University, Hangzhou Shi, Zhejiang, China

    Yuyu Yin

  4. London South Bank University, London, UK

    Muddesar Iqbal

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Ji, C., Zou, X., Hu, Y., Liu, S. (2019). A 2D Transform Based Distance Function for Time Series Classification. In: Gao, H., Wang, X., Yin, Y., Iqbal, M. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 268. Springer, Cham. https://doi.org/10.1007/978-3-030-12981-1_38

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  • DOI: https://doi.org/10.1007/978-3-030-12981-1_38

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-12981-1

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