Pi-Change: A Prior-Informed Multiple Change Point Detection Algorithm

TL;DR

Pi-Change is a novel algorithm for multiple change point detection that incorporates prior knowledge through a time-varying penalty, improving accuracy and robustness in time-series analysis.

Contribution

It introduces a prior-informed penalty within the Pruned Exact Linear Time framework, enabling efficient and interpretable multiple change point detection with prior information.

Findings

Pi-Change discourages spurious change points unsupported by priors.

It remains robust to prior misspecification.

It improves detection accuracy in simulations and real applications.

Abstract

Statistical change point (CP) detection methods typically rely on likelihood-based inference and ignore contextual information about plausible CP locations beyond the observed sequence. Although informative priors provide a natural way to incorporate such information, general and computationally efficient methods for doing so are lacking, especially for multiple CP detection. To address this gap, we propose a prior-informed CP detection algorithm (Pi-Change) that incorporates prior information on CP locations through a time-varying penalty term. We prove that the proposed penalty can be embedded in the Pruned Exact Linear Time framework while preserving the dynamic programming recursion and pruning rule required for efficient multiple CP detection. Across simulation studies and three time-series applications, Pi-Change discourages spurious CPs unsupported by prior information, remains robust to prior misspecification, and improves detection accuracy. More broadly, Pi-Change extends multiple CP detection beyond purely data-driven fitting by incorporating partial prior knowledge in a computationally efficient and interpretable way. It is particularly useful when CPs arise from heterogeneous mechanisms or are associated with known external events, helping quantify the delay between an event and the resulting structural change.