影响因子：1.247
DOI码：10.1002/sam.11448
发表刊物：Statistical Analysis and Data Mining
关键字：dynamic programming; factor analysis; incremental clustering; multivariate time series segmentation
摘要：To improve the efficiency of segmentation methods for multivariate time series, a hybrid dynamic learning mechanism for such series' segmentation is proposed. First, an incremental clustering algorithm is used to automatically cluster variables of multivariate time series. Second, common factors are extracted from every cluster by a dynamic factor model as an ensemble description of the system. Third, this common factor series is segmented by dynamic programming. The proposed method can potentially segment multivariate time series and not only performs segmentation better on multivariate time series with a large number of variables but also improves the running accuracy and efficiency of the algorithm, especially when analyzing complex datasets.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：13
期号：2
页面范围：165 - 177
ISSN号：19321864
是否译文：否
发表时间：2020-01-01
收录刊物：SCI
发布期刊链接：https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11448