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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.