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个人信息Personal Information
教授
博士生导师
硕士生导师
性别:男
毕业院校:东北大学
学位:博士
所在单位:控制科学与工程学院
学科:应用数学. 应用数学. 控制理论与控制工程
办公地点:创新园大厦A0620
联系方式:电话: (+86-411) 84726020 (home) (+86-411) 84709380 (Office) 传真: (+86-411) 84707579 手机: (+86-411) 13130042458
电子邮箱:xdliuros@dlut.edu.cn
A hybrid segmentation method for multivariate time series based on the dynamic factor model
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论文类型:期刊论文
发表时间:2017-08-01
发表刊物:STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
收录刊物:SCIE、EI、SSCI、Scopus
卷号:31
期号:6
页面范围:1291-1304
ISSN号:1436-3240
关键字:Change point; Common factor; Kalman filter; Segmentation
摘要:There have been a slew of ready-made methods for the segmentation of univariate time series, but in contrast, there are fewer segmentation methods to satisfy the demand for multivariate time series analysis. It has become a common practice to develop more segmentation methods for multivariate time series by extending segmentation methods of univariate time series. But on the contrary, this paper tries to reduce multivariate time series to a univariate common factor sequence to adapt to the methods for segmentation of univariate time series. First, a common factor sequence is extracted from the multivariate time series as a composite index by a dynamic factor model. Then, three typical search methods including binary segmentation, segment neighborhoods and the pruned exact linear time are applied to the common factor sequence to detect the change points and the segmentation result is considered as the final segmentation result of multivariate time series. The case studies show the applicability and robustness of the proposed approach in hydrometeorological time series segmentation.