Change Point Detection

Change point detection (CPD) aims to identify abrupt shifts in the statistical properties of time series data, crucial for understanding dynamic systems across diverse fields. Current research emphasizes developing robust and efficient algorithms, including Bayesian methods, kernel-based approaches like KCUSUM, and deep learning techniques that leverage recurrent neural networks or graph neural networks for handling complex data structures like dynamic graphs. These advancements improve accuracy and speed, particularly in high-dimensional or online settings, enabling timely responses to changes in areas such as finance, healthcare, and infrastructure monitoring.