Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems

Handle URI:
http://hdl.handle.net/10754/581691
Title:
Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems
Authors:
Kadri, Farid; Harrou, Fouzi; Chaabane, Sondès; Sun, Ying ( 0000-0001-6703-4270 ) ; Tahon, Christian
Abstract:
Monitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Seasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems 2015 Neurocomputing
Publisher:
Elsevier BV
Journal:
Neurocomputing
Issue Date:
22-Oct-2015
DOI:
10.1016/j.neucom.2015.10.009
Type:
Article
ISSN:
09252312
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0925231215014654
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKadri, Fariden
dc.contributor.authorHarrou, Fouzien
dc.contributor.authorChaabane, Sondèsen
dc.contributor.authorSun, Yingen
dc.contributor.authorTahon, Christianen
dc.date.accessioned2015-11-04T06:42:16Zen
dc.date.available2015-11-04T06:42:16Zen
dc.date.issued2015-10-22en
dc.identifier.citationSeasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systems 2015 Neurocomputingen
dc.identifier.issn09252312en
dc.identifier.doi10.1016/j.neucom.2015.10.009en
dc.identifier.urihttp://hdl.handle.net/10754/581691en
dc.description.abstractMonitoring complex production systems is primordial to ensure management, reliability and safety as well as maintaining the desired product quality. Early detection of emergent abnormal behaviour in monitored systems allows pre-emptive action to prevent more serious consequences, to improve system operations and to reduce manufacturing and/or service costs. This study reports the design of a new methodology for the detection of abnormal situations based on the integration of time-series analysis models and statistical process control (SPC) tools for the joint development of a monitoring system to help supervising of the behaviour of emergency department services (EDs). The monitoring system developed is able to provide early alerts in the event of abnormal situations. The seasonal autoregressive moving average (SARMA)-based exponentially weighted moving average (EWMA) anomaly detection scheme proposed was successfully applied to the practical data collected from the database of the paediatric emergency department (PED) at Lille regional hospital centre, France. The method developed utilizes SARMA as a modelling framework and EWMA for anomaly detection. The EWMA control chart is applied to the uncorrelated residuals obtained from the SARMA model. The detection results of the EWMA chart are compared with two other commonly applied residual-based tests: a Shewhart individuals chart and a Cumulative Sum (CUSUM) control chart.en
dc.language.isoenen
dc.publisherElsevier BVen
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0925231215014654en
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neurocomputing, 22 October 2015, DOI: 10.1016/j.neucom.2015.10.009en
dc.subjectEmergency departmenten
dc.subjectSPC schemesen
dc.subjectTime seriesen
dc.subjectAnomaly detectionen
dc.subjectSARMAen
dc.subjectEWMA control schemeen
dc.titleSeasonal ARMA-based SPC charts for anomaly detection: Application to emergency department systemsen
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalNeurocomputingen
dc.eprint.versionPost-printen
dc.contributor.institutionPIMM Laboratory, UMR CNRS 800, Arts et Métiers ParisTech, Paris, Franceen
dc.contributor.institutionLAMIH Research Center UMR CNRS 8201, UVHC, Le Mont Houy, F-59313 Valenciennes, Franceen
dc.contributor.affiliationKing Abdullah University of Science and Technology (KAUST)en
kaust.authorHarrou, Fouzien
kaust.authorSun, Yingen
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