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From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations

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Advanced Data Mining and Applications (ADMA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14176))

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Abstract

Multivariate time series classification is crucial for various applications such as activity recognition, disease diagnosis, and brain-computer interfaces. Deep learning methods have recently achieved promising performance thanks to their powerful representation learning capacity. However, existing deep learning-based classifiers rely solely on temporal information while disregarding clues from the frequency perspective. In this regard, we propose a novel method for classifying multivariate time series leveraging both temporal and frequency information. We first apply Short-Time Fourier Transform (STFT) to transform time series into spectrograms, which contain a 2D representation of frequency components and their temporal positions. In particular, for each variable, we generate spectrograms with varying frequencies and temporal resolutions under different window sizes. The transformation essentially adds a new modality to 1D time series and converts the multivariate time series classification into a multi-modality data classification task, making it possible to bring powerful backbones from computer vision fields to solve the time series classification problem. We then construct a dual-stream network based on the ResNet architecture that takes in both 1D and 2D representations for accurate multivariate time series classification. Our extensive experiments on 30 public datasets show our method outperforms multiple competitive state-of-the-art baselines.

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Technology Sydney, Ultimo, Australia

    Chao Yang & Xianzhi Wang

  2. School of Computer Science and Engineering, UNSW, Kensington, Australia

    Lina Yao

  3. Australian AI Institute, University of Technology Sydney, Ultimo, Australia

    Guodong Long

  4. Data Science Institute, University of Technology Sydney, Ultimo, Australia

    Guandong Xu

  5. CSIRO Data61, Canberra, Australia

    Lina Yao

Authors
  1. Chao Yang
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  2. Xianzhi Wang
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  3. Lina Yao
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  4. Guodong Long
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  5. Guandong Xu
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Corresponding author

Correspondence to Chao Yang .

Editor information

Editors and Affiliations

  1. Northeastern University, Shenyang, China

    Xiaochun Yang

  2. The University of Indonesia, Depok, Indonesia

    Heru Suhartanto

  3. Beijing Institute of Technology, Beijing, China

    Guoren Wang

  4. Northeastern University, Shenyang, China

    Bin Wang

  5. University of Technology Sydney, Sydney, NSW, Australia

    Jing Jiang

  6. Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Bing Li

  7. Sun Yat-sen University, Guangzhou, China

    Huaijie Zhu

  8. Anhui University, Hefei, China

    Ningning Cui

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Yang, C., Wang, X., Yao, L., Long, G., Xu, G. (2023). From Time Series to Multi-modality: Classifying Multivariate Time Series via Both 1D and 2D Representations. In: Yang, X., et al. Advanced Data Mining and Applications. ADMA 2023. Lecture Notes in Computer Science(), vol 14176. Springer, Cham. https://doi.org/10.1007/978-3-031-46661-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-46661-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46660-1

  • Online ISBN: 978-3-031-46661-8

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