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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 9549323, 7 pages
Research Article

A Cycle Deep Belief Network Model for Multivariate Time Series Classification

1School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China
2School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221116, China

Correspondence should be addressed to Gang Hua

Received 27 May 2017; Revised 11 July 2017; Accepted 24 August 2017; Published 4 October 2017

Academic Editor: M. L. R. Varela

Copyright © 2017 Shuqin Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Multivariate time series (MTS) data is an important class of temporal data objects and it can be easily obtained. However, the MTS classification is a very difficult process because of the complexity of the data type. In this paper, we proposed a Cycle Deep Belief Network model to classify MTS and compared its performance with DBN and KNN. This model utilizes the presentation learning ability of DBN and the correlation between the time series data. The experimental results showed that this model outperforms other four algorithms: DBN, KNN_ED, KNN_DTW, and RNN.