Sequential Change Point Detection

Sequential change point detection focuses on identifying abrupt shifts in data streams, aiming for rapid detection with minimal false alarms. Current research emphasizes robust methods handling diverse data types (e.g., multivariate, autocorrelated, heavy-tailed) and incorporating advanced models like state-space models, particle filters, and confidence sequences for improved accuracy and efficiency. These advancements are crucial for applications ranging from anomaly detection in sensor networks and supply chains to monitoring the performance of machine learning models in real-world deployments.