A Cycle Deep Belief Network Model for Multivariate Time Series Classification

作者：Wang, Shuqin; Hua, Gang^*; Hao, Guosheng; Xie, Chunli

来源：Mathematical Problems in Engineering, 2017, 2017: 9549323.

DOI：10.1155/2017/9549323

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.

• 出版日期2017
• 单位中国矿业大学（北京）