Year: 2022
Author: Min Li, Yu-Mei Huang
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 2 : pp. 353–366
Abstract
A multivariate time series segmentation model based on the minimization of the negative log-likelihood function of the series is proposed. The model is regularized by the $L_0$-norm of the time series mean change and solved by an alternating process. We use a dynamic programming algorithm in order to determine the breakpoints and the cross-validation method to find the parameters of the model. Experiments show the efficiency of the method for segmenting both synthetic and real multivariate time series.
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Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/10.4208/eajam.180921.050122
East Asian Journal on Applied Mathematics, Vol. 12 (2022), Iss. 2 : pp. 353–366
Published online: 2022-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 14
Keywords: Multivariate time series segmentation $L_0$-norm dynamic programming.