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论文类型：期刊论文

发表时间：2015-01-01

发表刊物：STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

收录刊物：SCIE、EI、Scopus

卷号：29

期号：1

页面范围：265-273

ISSN号：1436-3240

关键字：Multivariate time series; Segmentation; Change point; Dynamic programming; Threshold autoregressive model

摘要：In this paper, dynamic programming (DP) algorithm is applied to automatically segment multivariate time series. The definition and recursive formulation of segment errors of univariate time series are extended to multivariate time series, so that DP algorithm is computationally viable for multivariate time series. The order of autoregression and segmentation are simultaneously determined by Schwarz's Bayesian information criterion. The segmentation procedure is evaluated with artificially synthesized and hydrometeorological multivariate time series. Synthetic multivariate time series are generated by threshold autoregressive model, and in real-world multivariate time series experiment we propose that besides the regression by constant, autoregression should be taken into account. The experimental studies show that the proposed algorithm performs well.