Online change detection techniques in time series: an overview

dc.contributor.authorNamoano, Bernadin
dc.contributor.authorStarr, Andrew
dc.contributor.authorEmmanouilidis, Christos
dc.contributor.authorCristobal, Ruiz Carcel
dc.date.accessioned2021-04-30T10:58:15Z
dc.date.available2021-04-30T10:58:15Z
dc.date.issued2019-08-29
dc.description.abstractTime-series change detection has been studied in several fields. From sensor data, engineering systems, medical diagnosis, and financial markets to user actions on a network, huge amounts of temporal data are generated. There is a need for a clear separation between normal and abnormal behaviour of the system in order to investigate causes or forecast change. Characteristics include irregularities, deviations, anomalies, outliers, novelties or surprising patterns. The efficient detection of such patterns is challenging, especially when constraints need to be taken into account, such as the data velocity, volume, limited time for reacting to events, and the details of the temporal sequence.This paper reviews the main techniques for time series change point detection, focusing on online methods. Performance criteria including complexity, time granularity, and robustness is used to compare techniques, followed by a discussion about current challenges and open issuesen_UK
dc.identifier.citationNamoano B, Starr A, Emmanouilidis C, Ruiz Carcel C. (2019) Online change detection techniques in time series: an overview. In: 2019 IEEE International Conference on Prognostics and Health Management (ICPHM), 17-20 June 2019, San Francisco, USAen_UK
dc.identifier.urihttps://doi.org/10.1109/ICPHM.2019.8819394
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16638
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectOnline change detectionen_UK
dc.subjecttime series segmentationen_UK
dc.subjectabnormality detectionen_UK
dc.titleOnline change detection techniques in time series: an overviewen_UK
dc.typeConference paperen_UK

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