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

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• 作者：Yi Zheng ; Qi Liu ; Enhong Chen ; Yong Ge ; J. Leon Zhao
• 关键词：convolutional neural networks ; time series classification ; feature learning ; deep learning
• 刊名：Frontiers of Computer Science in China
• 出版年：2016
• 出版时间：February 2016
• 年：2016
• 卷：10
• 期：1
• 页码：96-112
• 全文大小：994 KB
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• 作者单位：Yi Zheng (1) (3)
  Qi Liu (1)
  Enhong Chen (1)
  Yong Ge (2)
  J. Leon Zhao (3)
  
  1. School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230027, China
  3. Department of Information Systems, City University of Hong Kong, Hong Kong, China
  2. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, 28223, USA
  
• 刊物类别：Computer Science
• 刊物主题：Computer Science, general
  Chinese Library of Science
  
• 出版者：Higher Education Press, co-published with Springer-Verlag GmbH
• ISSN：1673-7466

文摘

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. Keywords convolutional neural networks time series classification feature learning deep learning