@Article{EAJAM-12-2,
author = {Min, Li and Yu-Mei, Huang},
title = {An $L_0$-Norm Regularized Method for Multivariate Time Series Segmentation},
journal = {East Asian Journal on Applied Mathematics},
year = {2022},
volume = {12},
number = {2},
pages = {353--366},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {2079-7370},
doi = {https://doi.org/10.4208/eajam.180921.050122},
url = {https://global-sci.com/article/82470/an-l-0-norm-regularized-method-for-multivariate-time-series-segmentation}
}