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.
You do not have full access to this article.
Already a Subscriber? Sign in as an individual or via your institution
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.
Author Details
-
Memetic segmentation based on variable lag aware for multivariate time series
Wang, Ling
Shen, Peng
Information Sciences, Vol. 657 (2024), Iss. P.120003
https://doi.org/10.1016/j.ins.2023.120003 [Citations: 3]