Change-point detection with deep learning: A review

Abstract

Recent advances in deep learning have led to the creation of various methods for change-point detection (CPD). These methods enhance the ability of CPD techniques to handle complex, high-dimensional data, making them more adaptable and less dependent on strict assumptions about data distributions. CPD methods have also demonstrated high accuracy and have been applied across various fields, including manufacturing, healthcare, activity monitoring, finance, and environmental monitoring. This review provides an overview of how these methods are applied, the data sets they use, and how their performance is evaluated. It also organizes techniques into supervised and unsupervised categories, citing key studies. Finally, we explore ongoing challenges and suggest directions for future research to improve interpretability, generalizability, and real-world implementation.

Graphical abstract

Keywords

change-point detection / deep learning / supervised learning / unsupervised learning / time-series analysis

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