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

Yi Zheng, Qi Liu, Enhong Chen, Yong Ge, J. Leon Zhao

Research output: Contribution to journalArticlepeer-review

202 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)96-112
Number of pages17
JournalFrontiers of Computer Science
Issue number1
StatePublished - Feb 1 2016
Externally publishedYes


  • convolutional neural networks
  • deep learning
  • feature learning
  • time series classification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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