Enhancing Change Point Detection with Skew-t Distributions: Applications in Finance, Healthcare & AI

Music City Center 

Change point detection (CPD) is essential in identifying structural shifts in time-series data, with applications spanning finance, healthcare, and environmental monitoring. Traditional CPD methods often assume normality, which fails to capture real-world data that exhibit skewness and heavy tails. This talk explores using skew-t distributions in CPD, providing a more robust framework for detecting distributional shifts.

We introduce parametric and non-parametric CPD approaches, emphasizing a Bayesian Information Criterion (BIC)-based method tailored for skewed data. Applications include changes in financial market regimes, environmental monitoring of heavy metal contamination, and healthcare analytics such as glaucoma progression modeling. Additionally, we highlight the integration of CPD in machine learning and AI, including concept drift detection, anomaly detection, and reinforcement learning.

By leveraging skew-t distributions, we enhance the accuracy of CPD models in capturing asymmetric and long-tailed data, offering more reliable insights across disciplines.

Change Point Detection, Skew-T Distribution, Bayesian Information Criterion, Machine Learning, AI Model Adaptation, Concept Drift, and Anomaly Detection. 

ENAR