Segmentation of multivariate time series with factor model and dynamic programming

被引：0
作者：
Wang L. [1 ,2 ]
Xu P.-P. [1 ,2 ]
Peng K.-X. [1 ,2 ]
机构：
[1] School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing
[2] Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education, University of Science and Technology Beijing, Beijing
来源：
Kongzhi yu Juece/Control and Decision | 2020年 / 35卷 / 01期
关键词：
Dynamic programming; Factor analysis; Incremental clustering; Multivariate time series segmentation;
D O I：
10.13195/j.kzyjc.2018.0535
中图分类号：
学科分类号：
摘要：
The classical dynamic programming based segmentation algorithm is only suitable for low dimensional time series. To solve this problem, a segmentation method of multivariate time series with factor model and dynamic programming is proposed. Firstly, incremental clustering is used to automatically cluster variable sequences with similar trend. Then, a dynamic factor model is introduced to make the low-dimension multivariate time series obtained after dimension reduction reflect the overall trend of the original multivariate time series. Finally, the segmentation of high-dimension multivariate time series in the framework of low-dimension time series is realized by using dynamic programming. The experimental studies show that the proposed method has a good segmentation effect on multivariate time series data with a large number of variables. © 2020, Editorial Office of Control and Decision. All right reserved.
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页码：35 / 44
页数：9
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