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论文类型：期刊论文

发表时间：2016-10-01

发表刊物：STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

收录刊物：SCIE、EI、Scopus

卷号：30

期号：7

页面范围：1875-1887

ISSN号：1436-3240

关键字：Time series segmentation; Fuzzy clustering; Dynamic time warping; Dynamic programming

摘要：In this study, we propose a new segmentation algorithm to partition univariate and multivariate time series, where fuzzy clustering is realized for the segments formed in this way. The clustering algorithm involves a new objective function, which incorporates an extra variable related to segmentation, while dynamic time warping (DTW) is applied to determine distances between non-equal-length series. As optimizing the introduced objective function is a challenging task, we put forward an effective approach using dynamic programming (DP) algorithm. When calculating the DTW distance, a DP-based method is developed to reduce the computational complexity. In a series of experiments, both synthetic and real-world time series are used to evaluate the performance of the proposed algorithm. The results demonstrate higher effectiveness and advantages of the constructed algorithm when compared with the existing segmentation approaches.