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Exploiting multi-channels deep convolutional neural networks for multivariate time series classification

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Abstract

Time series classification is related to many different domains, such as health informatics, finance, and bioinformatics. Due to its broad applications, researchers have developed many algorithms for this kind of tasks, e.g., multivariate time series classification. Among the classification algorithms, k-nearest neighbor (k-NN) classification (particularly 1-NN) combined with dynamic time warping (DTW) achieves the state of the art performance. The deficiency is that when the data set grows large, the time consumption of 1-NN with DTWwill be very expensive. In contrast to 1-NN with DTW, it is more efficient but less effective for feature-based classification methods since their performance usually depends on the quality of hand-crafted features. In this paper, we aim to improve the performance of traditional feature-based approaches through the feature learning techniques. Specifically, we propose a novel deep learning framework, multi-channels deep convolutional neural networks (MC-DCNN), for multivariate time series classification. This model first learns features from individual univariate time series in each channel, and combines information from all channels as feature representation at the final layer. Then, the learnt features are applied into a multilayer perceptron (MLP) for classification. Finally, the extensive experiments on real-world data sets show that our model is not only more efficient than the state of the art but also competitive in accuracy. This study implies that feature learning is worth to be investigated for the problem of time series classification.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China

    Yi Zheng, Qi Liu & Enhong Chen

  2. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, 28223, USA

    Yong Ge

  3. Department of Information Systems, City University of Hong Kong, Hong Kong, China

    Yi Zheng & J. Leon Zhao

Authors
  1. Yi Zheng
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  2. Qi Liu
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  3. Enhong Chen
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  4. Yong Ge
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  5. J. Leon Zhao
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Corresponding author

Correspondence to Enhong Chen.

Additional information

Yi Zheng received his BE in Computer Science and Technology from Harbin Institute of Technology, China in 2009. He is currently a PhD student in the School of Computer Science and Technology at University of Science and Technology of China (USTC), China. His major research interests include time series data mining and deep learning. He has published several papers in refereed conference proceedings and journals, such as WAIM’14, PAKDD’15 and Nature Communications.

Qi Liu is an associate researcher in University of Science and Technology of China (USTC), China. He received his PhD in Computer Science from USTC. His general area of research is data mining and knowledge discovery. He has published prolifically in refereed journals and conference proceedings, e.g., TKDE, TOIS, TKDD, TIST, SIGKDD, IJCAI, ICDM, and CIKM. He has served regularly in the program committees of a number of conferences, and is a reviewer for the leading academic journals in his fields. He is a member of ACM and IEEE. Dr. Liu is the recipient of the ICDM’11 Best Research Paper Award, the Special Prize of President Scholarship for Postgraduate Students, Chinese Academy of Sciences (CAS) and the Distinguished Doctoral Dissertation Award of CAS.

Enhong Chen received his PhD in Computer Science from University of Science and Technology of China (USTC), China, his MS from Hefei University of Technology, China and his BS from Anhui University, China. He is currently a professor and the vice dean of the School of Computer Science, the vice director of the National Engineering Laboratory for Speech and Language Information Processing of USTC, winner of the National Science Fund for Distinguished Young Scholars of China. His research interests include data mining and machine learning, social network analysis and recommender systems. He has published lots of papers on refereed journals and conferences, including IEEE TKDE, TMC, KDD, ICDM, NIPS, CIKM and Nature Communications. He was on program committees of numerous conferences including KDD, ICDM, SDM. He received the Best Application Paper Award on KDD’08 and Best Research Paper Award on ICDM’11. He is a senior member of the IEEE.

Yong Ge received his PhD in Information Technology from Rutgers, The State University of New Jersey, USA in 2013, hisMS in Signal and Information Processing from the University of Science and Technology of China (USTC), China in 2008, and his BE in Information Engineering from Xi’an Jiao Tong University, China in 2005. He is currently an assistant professor at the University of North Carolina at Charlotte, USA. His research interests include data mining and business analytics. He has published prolifically in refereed journals and conference proceedings, such as IEEE TKDE, ACM TOIS, ACM TKDD, ACMTIST, ACMSIGKDD, SIAM SDM, IEEE ICDM, and ACM RecSys.

J. Leon Zhao is the Head and Chair Professor in the Department of Information Systems, City University of Hong Kong, China. He was Interim Head and Eller Professor in Management Information Systems, University of Arizona, USA. He holds PhD from Haas School of Business, University of California at Berkeley, USA. His research is on information technology and management, with a particular focus on collaboration and workflow technologies and business information services. He is director of Lab on Enterprise Process Innovation and Computing funded by NSF, RGC, SAP, and IBM among other sponsors. He received IBM Faculty Award in 2005 and was awarded Chang Jiang Scholar Chair Professorship at Tsinghua University in 2009.

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Zheng, Y., Liu, Q., Chen, E. et al. Exploiting multi-channels deep convolutional neural networks for multivariate time series classification. Front. Comput. Sci. 10, 96–112 (2016). https://doi.org/10.1007/s11704-015-4478-2

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  • DOI: https://doi.org/10.1007/s11704-015-4478-2

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