Relating and comparing methods for detecting changes in mean

Paul Fearnhead · Guillem J. Rigaill

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Statistical Methods and Inference

Abstract

In recent years, there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their applications. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their locations, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.

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References

2012Optimal Detection of Changepoints With a Linear Computational Cost 2019Selective review of offline change point detection methods 2014Wild binary segmentation for multiple change-point detection 1988Estimating the number of change-points via Schwarz' criterion 1971Inference about the Change-Point from Cumulative Sum Tests 2012Structural breaks in time series 2014Multiscale Change Point Inference 2016On optimal multiple changepoint algorithms for large data

Cited by

2024cpop: Detecting Changes in Piecewise-Linear Signals 2024Data segmentation for time series based on a general moving sum approach 2023gfpop: An R Package for Univariate Graph-Constrained Change-Point Detection 2022Scalable change-point and anomaly detection in cross-correlated data with an application to condition monitoring 2023A Log-Linear Nonparametric Online Changepoint Detection Algorithm Based on Functional Pruning

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