Version 1
: Received: 10 April 2023 / Approved: 11 April 2023 / Online: 11 April 2023 (09:41:04 CEST)
How to cite:
CHEN, Z.; XIE, N.M. Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method. Preprints2023, 2023040217. https://doi.org/10.20944/preprints202304.0217.v1
CHEN, Z.; XIE, N.M. Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method. Preprints 2023, 2023040217. https://doi.org/10.20944/preprints202304.0217.v1
CHEN, Z.; XIE, N.M. Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method. Preprints2023, 2023040217. https://doi.org/10.20944/preprints202304.0217.v1
APA Style
CHEN, Z., & XIE, N.M. (2023). Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method. Preprints. https://doi.org/10.20944/preprints202304.0217.v1
Chicago/Turabian Style
CHEN, Z. and Nini Mrs. XIE. 2023 "Structural Change-Point Detection for Time Series via Support Vector Regression and Self-Normalization Method" Preprints. https://doi.org/10.20944/preprints202304.0217.v1
Abstract
This study considers the change-point test problem for time series based on the self-normalization ratio statistic test, which is constructed using residuals obtained from a support vector regression (SVR)-autoregressive moving average (ARMA) model. Under the null hypothesis, the series is a stationary process, and our test statistic converges to a non-degenerate distribution. Under the alternative hypothesis, there are change-points in the time series, and the self-normalization test statistic diverges to infinity. The simulations show that our proposed new test has better finite sample performance than other SVR-based tests in the literature. Finally, we illustrate its usefulness by analyzing two actual data sets.
Computer Science and Mathematics, Probability and Statistics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
