A Cycle Deep Belief Network Model for Multivariate Time Series Classification. (4th October 2017)

• Record Type: Journal Article
• Authors: Wang, Shuqin; Hua, Gang; Hao, Guosheng; Xie, Chunli
• Other Names: Varela M. L. R. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2017(2017)
• Publication Date: 2017-10-04
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2017/9549323
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : 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.