Bayesian Online Prediction of Change Points

Diego Agudelo-España, Sebastian Gomez-Gonzalez, Stefan Bauer, Bernhard Schölkopf, Jan Peters
; Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR 124:320-329, 2020.

Abstract

Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.

Cite this Paper


BibTeX
@InProceedings{pmlr-v124-agudelo-espana20a, title = {Bayesian Online Prediction of Change Points}, author = {Agudelo-Espa\~{n}a, Diego and Gomez-Gonzalez, Sebastian and Bauer, Stefan and Sch\"{o}lkopf, Bernhard and Peters, Jan}, pages = {320--329}, year = {2020}, editor = {Jonas Peters and David Sontag}, volume = {124}, series = {Proceedings of Machine Learning Research}, address = {Virtual}, month = {03--06 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v124/agudelo-espana20a/agudelo-espana20a.pdf}, url = {http://proceedings.mlr.press/v124/agudelo-espana20a.html}, abstract = {Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.} }
Endnote
%0 Conference Paper %T Bayesian Online Prediction of Change Points %A Diego Agudelo-España %A Sebastian Gomez-Gonzalez %A Stefan Bauer %A Bernhard Schölkopf %A Jan Peters %B Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) %C Proceedings of Machine Learning Research %D 2020 %E Jonas Peters %E David Sontag %F pmlr-v124-agudelo-espana20a %I PMLR %J Proceedings of Machine Learning Research %P 320--329 %U http://proceedings.mlr.press %V 124 %W PMLR %X Online detection of instantaneous changes in the generative process of a data sequence generally focuses on retrospective inference of such change points without considering their future occurrences. We extend the Bayesian Online Change Point Detection algorithm to also infer the number of time steps until the next change point (i.e., the residual time). This enables to handle observation models which depend on the total segment duration, which is useful to model data sequences with temporal scaling. The resulting inference algorithm for segment detection can be deployed in an online fashion, and we illustrate applications to synthetic and to two medical real-world data sets.
APA
Agudelo-España, D., Gomez-Gonzalez, S., Bauer, S., Schölkopf, B. & Peters, J.. (2020). Bayesian Online Prediction of Change Points. Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI), in PMLR 124:320-329

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