The change point detection problem in the time series arises in a wide variety of fields. In some situations, we lack the necessary resources to apply complex techniques, so lightweight approaches are needed.In this study, we consider lightweight approaches to the change point detection problem, namely, classification-based barrier methods. We want to investigate the use of various classifiers and classification evaluation metrics for change point detection, so we are creating a framework that makes it easy to build methods from different components. To study a large number of constructed methods, we create a flexible benchmarking system that allows one to evaluate methods using different metrics.We conduct an empirical study of the methods, present the results and compare them with existing methods and with each other. Our implementation of the KNN based method shows high-quality results. However, we see potential in using at least one more tested classifier as well.
Original languageEnglish
Title of host publication2025 37th Conference of Open Innovations Association (FRUCT)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages213-220
Number of pages8
ISBN (Electronic)978-952-65246-3-4
ISBN (Print)9789526524634
DOIs
StatePublished - 14 May 2025
EventThe 37th FRUCT conference: FRUCT37 - UiT The Arctic University of Norway, Kufstein, Austria
Duration: 14 May 202516 May 2025
https://www.fruct.org/conferences/37/registration/

Publication series

NameConference of Open Innovation Association, FRUCT

Conference

ConferenceThe 37th FRUCT conference
Abbreviated titleFRUCT37
Country/TerritoryAustria
CityKufstein
Period14/05/2516/05/25
Internet address

ID: 137266748