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An exact approach to Bayesian sequential change point detection

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  • Ruggieri, Eric
  • Antonellis, Marcus

Abstract

Change point models seek to fit a piecewise regression model with unknown breakpoints to a data set whose parameters are suspected to change through time. However, the exponential number of possible solutions to a multiple change point problem requires an efficient algorithm if long time series are to be analyzed. A sequential Bayesian change point algorithm is introduced that provides uncertainty bounds on both the number and location of change points. The algorithm is able to quickly update itself in linear time as each new data point is recorded and uses the exact posterior distribution to infer whether or not a change point has been observed. Simulation studies illustrate how the algorithm performs under various parameter settings, including detection speeds and error rates, and allow for comparison with several existing multiple change point algorithms. The algorithm is then used to analyze two real data sets, including global surface temperature anomalies over the last 130 years.

Suggested Citation

  • Ruggieri, Eric & Antonellis, Marcus, 2016. "An exact approach to Bayesian sequential change point detection," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 71-86.
    • Handle: RePEc:eee:csdana:v:97:y:2016:i:c:p:71-86
    DOI: 10.1016/j.csda.2015.11.010
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    Cited by:

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    2. Hongyue Zhu & Hong Jiao & Wei Gao & Xiangbin Meng, 2023. "Bayesian Change-Point Analysis Approach to Detecting Aberrant Test-Taking Behavior Using Response Times," Journal of Educational and Behavioral Statistics, , vol. 48(4), pages 490-520, August.
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    4. Daniel Felix Ahelegbey & Monica Billio & Roberto Casarin, 2020. "Modeling Turning Points In Global Equity Market," DEM Working Papers Series 195, University of Pavia, Department of Economics and Management.
    5. Aviral Kumar Tiwari & Cleiton Guollo Taufemback & Satish Kumar, 2021. "A Sequential Bayesian Change-Point Analysis of BRICS Currency Returns," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(2), pages 393-402, June.
    6. Muhammad Rizwan Khan & Biswajit Sarkar, 2019. "Change Point Detection for Airborne Particulate Matter ( PM 2.5 , PM 10 ) by Using the Bayesian Approach," Mathematics, MDPI, vol. 7(5), pages 1-42, May.
    7. Sanaz Moghim & Mohammad Sina Jahangir, 2022. "Reliability framework for characterizing heat wave and cold spell events," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(2), pages 1503-1525, June.
    8. Song, Chunhe & Jing, Wei & Zeng, Peng & Rosenberg, Catherine, 2017. "An analysis on the energy consumption of circulating pumps of residential swimming pools for peak load management," Applied Energy, Elsevier, vol. 195(C), pages 1-12.
    9. Rui Qiang & Eric Ruggieri, 2023. "Autocorrelation and Parameter Estimation in a Bayesian Change Point Model," Mathematics, MDPI, vol. 11(5), pages 1-22, February.

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