BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints

Alexandr Maslov, Mykola Pechenizkiy, Yulong Pel, Indre Zliobaite, Alexander Shklyaev, Tommi Karkkainen, Jaakko Hollmen

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)


Online changepoint detection is an important task for machine learning in changing environments, as it signals when the learning model needs to be updated. Presence of noise that can be mistaken for real changes makes it difficult to develop an effective approach that would have a low false alarm rate and being able to detect all the changes with a minimal delay. In this paper we study how performance of popular Bayesian online detectors can be improved in case of recurrent changes. Modelling recurrence allows us to anticipate future changepoints and predict their locations in time. We propose an approach for inducing and integrating recurrence information in the streaming settings, and demonstrate its effectiveness on synthetic and real-world human activity datasets.

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Number of pages8
ISBN (Electronic)9781509061815
Publication statusPublished - 30 Jun 2017
MoE publication typeA4 Article in a conference publication
EventInternational Joint Conference on Neural Networks - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronic Engineers
ISSN (Print)2161-4393
ISSN (Electronic)2161-4407


ConferenceInternational Joint Conference on Neural Networks
Abbreviated titleIJCNN
Country/TerritoryUnited States


  • Detectors
  • Bayes methods
  • Hazards
  • Mathematical model
  • Electronic mail
  • Data models
  • Noise measurement


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