Comprehensive analysis of change-point dynamics detection in time series data: A review

In the ever-evolving field of time series analysis, detecting changes in patterns and dynamics is paramount for accurate forecasting and meaningful insights. This article thoroughly explores several algorithms for detecting and analyzing pattern changes in time series data. The exploration covers a broad spectrum of algorithms, further venturing into their categorization based on functional modalities and the ability to identify complex changes. Recognizing pattern changes in time series data holds pivotal importance as it aids in anticipating future trends, ensuring efficient resource allocation, and mitigating potential challenges. This research goes beyond a basic overview and conducts a thorough comparative analysis, highlighting each algorithm’s strengths, drawbacks, and computational complexities. This comparative approach provides practitioners and researchers with the necessary information to select the most suitable algorithm for their requirements. Additionally, this review provides insight into potential future research directions, proposing possible improvements and breakthroughs in the design and application of algorithms. This review also provides a runtime analysis of various pattern change detection algorithms, presenting an in-depth evaluation of the existing methodologies. It serves as a vital reference for individuals dealing with the dynamic nature of time series data.