Impact Factor:  3.821

DOI number:  10.1007/s00477-020-01817-w

Journal:  Stochastic Environmental Research and Risk Assessment

Key Words:  Feature extraction; Fuzzy segmentation; G–G clustering; MDBI index; Multivariate time series

Abstract:  In this paper, Gath–Geva (G–G) fuzzy clustering is extended to adaptively segment hydrometeorological multivariate time series. First, KPCA is used to extract principle components of multivariate time series to remove the impacts of redundant and irrelevant variables. Then, taking the time information into account, the segmentation of principle components of multivariate time series is derived with the modified Davies–Bouldin Index and adaptive G–G fuzzy clustering. In the experiment, our proposed algorithm is applied on the real-world hydrometeorological multivariate time series collected every 6 min with length N= 720. Comparison with the existing segmentation algorithms, our proposed algorithm proves the applicability and usefulness in hydrometeorological multivariate time series analysis. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.

Indexed by:  Journal paper

Discipline:  Engineering

Document Type:  J

Volume:  34

Issue:  9

Page Number:  1353 - 1367

ISSN No.:  14363240

Translation or Not:  no

Date of Publication:  2020-01-01

Included Journals:  SCI

Links to published journals:  https://link.springer.com/article/10.1007/s00477-020-01817-w