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
相关论文
共 50 条
  • [41] Fuzzy segmentation of multivariate time series with KPCA and G-G clustering
    Wang L.
    Zhu H.
    Kongzhi yu Juece/Control and Decision, 2021, 36 (01): : 115 - 124
  • [42] Adaptive Segmentation of Multivariate Time Series with FastICA and G-G Clustering
    Wang L.
    Li Z.-Z.
    Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2023, 51 (05): : 1235 - 1244
  • [43] Joint segmentation of multivariate astronomical time series: Bayesian sampling with a hierarchical model
    Dobigeon, Nicolas
    Tourneret, Jean-Yves
    Scargle, Jeffrey D.
    IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2007, 55 (02) : 414 - 423
  • [44] Adaptive G-G clustering for fuzzy segmentation of multivariate time series
    Wang, Ling
    Zhu, Hui
    Jia, Gaofeng
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2020, 34 (09) : 1353 - 1367
  • [45] Generic Phase Space Reconstruction Method of Multivariate Time Series
    Kong, Lingshuang
    Yang, Chunhua
    Wang, Yalin
    Gui, Weihua
    CCDC 2009: 21ST CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-6, PROCEEDINGS, 2009, : 3752 - 3755
  • [46] Research on Pattern Matching Method of Multivariate Hydrological Time Series
    Gai, Zhen
    Lou, Yuansheng
    Ye, Feng
    Li, Ling
    DATA SCIENCE, PT 1, 2017, 727 : 64 - 72
  • [47] Study on the Analysis Method for Multivariate Time Series Based on the PCA
    Zhao An-xing
    Zhou Xiao-cheng
    Ma Xiang-ling
    ISTM/2009: 8TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1-6, 2009, : 299 - 302
  • [48] A multivariate time series segmentation algorithm for analyzing the operating statuses of tunnel boring machines
    Pang, Yong
    Shi, Maolin
    Zhang, Liyong
    Sun, Wei
    Song, Xueguan
    KNOWLEDGE-BASED SYSTEMS, 2022, 242
  • [49] SIMTSeg: A self-supervised multivariate time series segmentation method with periodic subspace projection and reverse diffusion for industrial process
    Bao, Xiangyu
    Zheng, Yu
    Zhong, Jingshu
    Chen, Liang
    ADVANCED ENGINEERING INFORMATICS, 2024, 62
  • [50] A Neural Networks Based Method for Multivariate Time-Series Forecasting
    Li, Shaowei
    Huang, He
    Lu, Wei
    IEEE ACCESS, 2021, 9 : 63915 - 63924
12345