A0242
Title: Online change point detection via copula based Markov models
Authors: Li-Hsien Sun - National Central University (Taiwan) [presenting]
Abstract: Time series analysis is a critical issue in varied fields such as finance, industry, and biology. However, due to the possibility of the structure change, the corresponding problem such as loss or damage can be expected. See the stock market during the financial crisis in 2008 and also the COVID-19 in 2020 for instance. Hence, the corresponding change point for structural change is worth to study. In order to detect the change point online for time series data or correlated data, we propose the model for online change point detection via copula-based Markov models where the time serial data is described by copula-based Markov model and the change point detection based on the run-length distribution using the Bayesian approach. Finally, the performance of the proposed method is illustrated through numerical and empirical studies.
