An L0-Norm Regularized Method for Multivariate Time Series Segmentation

被引：1
|
作者：
Li, Min [1 ]
Huang, Yu-Mei [1 ]
机构：
[1] Lanzhou Univ, Ctr Data Sci, Sch Math & Stat, Lanzhou 730000, Peoples R China
来源：
EAST ASIAN JOURNAL ON APPLIED MATHEMATICS | 2022年 / 12卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Multivariate time series; segmentation; L-0-norm; dynamic programming; HIDDEN MARKOV-MODELS; CHANGE-POINTS; PRECONDITIONERS; CHANGEPOINTS; ALGORITHM;
D O I：
10.4208/eajam.180921.050122
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
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.
引用
收藏
页码：353 / 366
页数：14
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