Impact Factor:  1.247

DOI number:  10.1002/sam.11448

Journal:  Statistical Analysis and Data Mining

Key Words:  dynamic programming; factor analysis; incremental clustering; multivariate time series segmentation

Abstract:  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.

Indexed by:  Journal paper

Discipline:  Engineering

Document Type:  J

Volume:  13

Issue:  2

Page Number:  165 - 177

ISSN No.:  19321864

Translation or Not:  no

Date of Publication:  2020-01-01

Included Journals:  SCI

Links to published journals:  https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11448