TY - JOUR
T1 - An $L_0$-Norm Regularized Method for Multivariate Time Series Segmentation
AU - Li , Min
AU - Huang , Yu-Mei
JO - East Asian Journal on Applied Mathematics
VL - 2
SP - 353
EP - 366
PY - 2022
DA - 2022/02
SN - 12
DO - http://doi.org/10.4208/eajam.180921.050122
UR - https://global-sci.org/intro/article_detail/eajam/20258.html
KW - Multivariate time series, segmentation, $L_0$-norm, dynamic programming.
AB - <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>