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Article: Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management

TitleForecasting time series of inhomogeneous Poisson processes with application to call center workforce management
Authors
KeywordsFactor model
Queueing systems
Dimension reduction
Service engineering
Singular value decomposition
Vector time series
Penalized likelihood
Forecast updating
Issue Date2008
Citation
Annals of Applied Statistics, 2008, v. 2, n. 2, p. 601-623 How to Cite?
AbstractWe consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analysis of Poisson variables, followed by time series modeling of factor score series. Time series forecasts of factor scores are combined with factor loadings to yield forecasts of future Poisson rate profiles. Penalized Poisson regressions on factor loadings guided by time series forecasts of factor scores are used to generate dynamic within-process rate updating. Methods are also developed to obtain distributional forecasts. Our methods are illustrated using simulation and real data. The empirical results demonstrate how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing. © Institute of Mathematical Statistics.
Persistent Identifierhttp://hdl.handle.net/10722/219603
ISSN
2015 Impact Factor: 1.432
2015 SCImago Journal Rankings: 1.533

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Jianhua Z.-
dc.date.accessioned2015-09-23T02:57:30Z-
dc.date.available2015-09-23T02:57:30Z-
dc.date.issued2008-
dc.identifier.citationAnnals of Applied Statistics, 2008, v. 2, n. 2, p. 601-623-
dc.identifier.issn1932-6157-
dc.identifier.urihttp://hdl.handle.net/10722/219603-
dc.description.abstractWe consider forecasting the latent rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operations management of queueing systems, in particular, telephone call centers, where accurate forecasting of call arrival rates is a crucial primitive for efficient staffing of such centers. Our forecasting approach utilizes dimension reduction through a factor analysis of Poisson variables, followed by time series modeling of factor score series. Time series forecasts of factor scores are combined with factor loadings to yield forecasts of future Poisson rate profiles. Penalized Poisson regressions on factor loadings guided by time series forecasts of factor scores are used to generate dynamic within-process rate updating. Methods are also developed to obtain distributional forecasts. Our methods are illustrated using simulation and real data. The empirical results demonstrate how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing. © Institute of Mathematical Statistics.-
dc.languageeng-
dc.relation.ispartofAnnals of Applied Statistics-
dc.subjectFactor model-
dc.subjectQueueing systems-
dc.subjectDimension reduction-
dc.subjectService engineering-
dc.subjectSingular value decomposition-
dc.subjectVector time series-
dc.subjectPenalized likelihood-
dc.subjectForecast updating-
dc.titleForecasting time series of inhomogeneous Poisson processes with application to call center workforce management-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1214/08-AOAS164-
dc.identifier.scopuseid_2-s2.0-69249117073-
dc.identifier.volume2-
dc.identifier.issue2-
dc.identifier.spage601-
dc.identifier.epage623-
dc.identifier.eissn1941-7330-

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