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.

在本文中，Gath-Geva（G-G）模糊聚类扩展到了自适应分段水文气象多元时间序列。首先，KPCA用于提取多元时间序列的主要成分，以消除冗余变量和无关变量的影响。然后，考虑到时间信息，使用改进的Davies-Bouldin指数和自适应G-G模糊聚类，得出多元时间序列主成分的分割。在实验中，我们提出的算法应用于每6分钟收集一次的真实世界水文气象多元时间序列，长度为\（N = 720 \）。与现有的分割算法相比，本文提出的算法证明了其在水文气象多元时间序列分析中的适用性和实用性。