影响因子：3.821
DOI码：10.1007/s00477-020-01817-w
发表刊物：Stochastic Environmental Research and Risk Assessment
关键字：Feature extraction; Fuzzy segmentation; G–G clustering; MDBI index; Multivariate time series
摘要：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.
论文类型：期刊论文
学科门类：工学
文献类型：J
卷号：34
期号：9
页面范围：1353 - 1367
ISSN号：14363240
是否译文：否
发表时间：2020-01-01
收录刊物：SCI
发布期刊链接：https://link.springer.com/article/10.1007/s00477-020-01817-w