Bayesian Online Change Point Detection for Baseline Shifts

  • Ginga Yoshizawa Intel Corp.
Keywords: Time series, Change point detection, Online detection


In time series data analysis, detecting change points on a real-time basis (online) is of great interest in many areas, such as finance, environmental monitoring, and medicine. One promising means to achieve this is the Bayesian online change point detection (BOCPD) algorithm, which has been successfully adopted in particular cases in which the time series of interest has a fixed baseline. However, we have found that the algorithm struggles when the baseline irreversibly shifts from its initial state. This is because with the original BOCPD algorithm, the sensitivity with which a change point can be detected is degraded if the data points are fluctuating at locations relatively far from the original baseline. In this paper, we not only extend the original BOCPD algorithm to be applicable to a time series whose baseline is constantly shifting toward unknown values but also visualize why the proposed extension works. To demonstrate the efficacy of the proposed algorithm compared to the original one, we examine these algorithms on two real-world data sets and six synthetic data sets.


Ryan Prescott Adams and David J. C. Mckay, Bayesian Online Changepoint Detection, arXiv:0710.3742v1 [stat.ML], 2007.

Samaneh Aminikhanghahi and Diane J. Cook, A survey of methods for time series change point detection, Knowl. Inf. Syst. 51, 339-367, 2017.

Amadou Ba and Sean A. McKenna, Water quality monitoring with online change-point detection methods, Journal of Hydroinformatics 17.1, 2015.

Hon Fai Lau and Shigeru Yamamoto, Bayesian Online Changepoint Detection to Improve Transparency in Human-Machine Interaction Systems, 49th IEEE Conference on Decision and Control, 2010.

Rakesh Malladi, Giridhar P Kalamangalam, Behnaam Aazhang, Online Bayesian Change Point Detection Algorithms for Segmentation of Epileptic Activity, IEEE Asilomar Conference on Signals, Systems and Computers, 2013.

Alan H. Gee, Joshua Chang, Joydeep Ghosh, David Paydarfar, Bayesian Online Changeopint Detection of Physiological Transitions, In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2018.

Octavian Niculita, Zakwan Skaf, Ian K. Jennions, The application of Bayesian Change Point Detection in UAV Fuel Systems, 3rd International Conference on Through-life Engineering Services, 2014.

Ryan Turner, Bayesian Change Point Detection For Satellite Fault Prediction, Proceedings of the interdisciplinary graduate conference, Cambridge University, UK, 2010.

Ryan Turner, Yunus Saatci, Carl Edward Rasmussen, Adaptive Sequential Bayesian Change Point Detection, In Advances in Neural Information Processing Systems (NeurIPS): Temporal Segmentation Workshop, 2009.

Eric Ruggieri and Marcus Antonellis, An exact approach to Bayesian sequential change point detection, Computational Statistics and Data Analysis 97 71-86, 2016.

Diego Agudelo-Espana, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Scholkopf, Jan Peters, Bayesian Online Detection and Prediction of Change Points, arXiv:1902.04524v1 [cs.LG], 2019.

Jean-Francois Ducre-Robitaille, Lucie A. Vincent, Gilles Boulet, Comparison of techniques for detection of discontinuities in temperature series, International Journal of Climatology, Int. J. Climatol. 23: 1087-1101, 2003.

Naoki Shimada, Time Series Analysis, Kyoritsu Shuppan, ISBN 978-4-320-12501-8, 2019.

K. P. Murphy, Conjugate Bayesian analysis of the gaussian distribution, Technical report, 2007.

Zielak, Bitcoin Historical Data, Version 4, License: Creative Commons Attribution 4.0 International (CC BY-SA 4.0), Retrieved October 22, 2020 from

COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University ( License: Creative Commons Attribution 4.0 International (CC BY 4.0), Retrieved October 22, 2020 from 19/blob/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv

Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Inf Dis. 20(5):533-534. doi: 10.1016/S1473 -3099(20)30120-1.

How to Cite
Yoshizawa, G. (2020). Bayesian Online Change Point Detection for Baseline Shifts. Statistics, Optimization & Information Computing, 9(1), 1-16.
Research Articles