The development of an information criterion for Change-Point Analysis
This work addresses the need for a unified and principled method for change-point analysis in noisy time series data, which is incremental as it builds on existing approaches.
The authors tackled the problem of testing for change points in time series data by developing a unified information-based approach that reconciles frequentist and information-based methods, resulting in a statistically principled, parameter-free method applicable to a wide range of change-point problems.
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observables are corrupted by noise. The change-point algorithm is used to identify the time indices (change points) at which the system transitions between these discrete states. We present a unified information-based approach to testing for the existence of change points. This new approach reconciles two previously disparate approaches to Change-Point Analysis (frequentist and information-based) for testing transitions between states. The resulting method is statistically principled, parameter and prior free and widely applicable to a wide range of change-point problems.