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Multivariate Time Series Early Classification with Interpretability Using Deep Learning and Attention Mechanism

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Advances in Knowledge Discovery and Data Mining (PAKDD 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11441))

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Abstract

Multivariate time-series early classification is an emerging topic in data mining fields with wide applications like biomedicine, finance, manufacturing, etc. Despite of some recent studies on this topic that delivered promising developments, few relevant works can provide good interpretability. In this work, we consider simultaneously the important issues of model performance, earliness, and interpretability to propose a deep-learning framework based on the attention mechanism for multivariate time-series early classification. In the proposed model, we used a deep-learning method to extract the features among multiple variables and capture the temporal relation that exists in multivariate time-series data. Additionally, the proposed method uses the attention mechanism to identify the critical segments related to model performance, providing a base to facilitate the better understanding of the model for further decision making. We conducted experiments on three real datasets and compared with several alternatives. While the proposed method can achieve comparable performance results and earliness compared to other alternatives, more importantly, it can provide interpretability by highlighting the important parts of the original data, rendering it easier for users to understand how the prediction is induced from the data.

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Notes

  1. 1.

    Bobski’s World: http://www.cs.cmu.edu/~bobski/.

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Acknowledgment

This research was partially supported by Ministry of Science and Technology, Taiwan, under grant no. 107-2218-E-009-005 and 107-2218-E-009-050.

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Authors and Affiliations

  1. Department of Computer Science, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan, ROC

    En-Yu Hsu & Vincent S. Tseng

  2. Department of Industrial Engineering and Management, National Chiao Tung University, 1001 University Road, Hsinchu, 300, Taiwan, ROC

    Chien-Liang Liu

Authors
  1. En-Yu Hsu
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  2. Chien-Liang Liu
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  3. Vincent S. Tseng
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Corresponding author

Correspondence to Vincent S. Tseng .

Editor information

Editors and Affiliations

  1. Hong Kong University of Science and Technology, Hong Kong, China

    Qiang Yang

  2. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  3. University of Macau, Taipa, Macau, China

    Zhiguo Gong

  4. Southeast University, Nanjing, China

    Min-Ling Zhang

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Sheng-Jun Huang

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Hsu, EY., Liu, CL., Tseng, V.S. (2019). Multivariate Time Series Early Classification with Interpretability Using Deep Learning and Attention Mechanism. In: Yang, Q., Zhou, ZH., Gong, Z., Zhang, ML., Huang, SJ. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2019. Lecture Notes in Computer Science(), vol 11441. Springer, Cham. https://doi.org/10.1007/978-3-030-16142-2_42

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  • DOI: https://doi.org/10.1007/978-3-030-16142-2_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-16141-5

  • Online ISBN: 978-3-030-16142-2

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