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Article: On modeling and forecasting time series of smooth curves

TitleOn modeling and forecasting time series of smooth curves
Authors
KeywordsDimension reduction
Factor models
Functional data analysis
Penalization
Shrinkage
Singular value decomposition
Issue Date2009
Citation
Technometrics, 2009, v. 51, n. 3, p. 227-238 How to Cite?
AbstractWe consider modeling a time series of smooth curves and develop methods for forecasting such curves and dynamically updating the forecasts. The research problem is motivated by efficient operations management of telephone customer service centers, where forecasts of daily call arrival rate profiles are needed for service agent staffing and scheduling purposes. Our methodology has three components: dimension reduction through a smooth factor model, time series modeling and forecasting of the factor scores, and dynamic updating using penalized least squares. The proposed methods are illustrated via the motivating application and two simulation studies. © 2009 American Statistical Association.
Persistent Identifierhttp://hdl.handle.net/10722/219604
ISSN
2015 Impact Factor: 1.435
2015 SCImago Journal Rankings: 1.018

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.date.accessioned2015-09-23T02:57:30Z-
dc.date.available2015-09-23T02:57:30Z-
dc.date.issued2009-
dc.identifier.citationTechnometrics, 2009, v. 51, n. 3, p. 227-238-
dc.identifier.issn0040-1706-
dc.identifier.urihttp://hdl.handle.net/10722/219604-
dc.description.abstractWe consider modeling a time series of smooth curves and develop methods for forecasting such curves and dynamically updating the forecasts. The research problem is motivated by efficient operations management of telephone customer service centers, where forecasts of daily call arrival rate profiles are needed for service agent staffing and scheduling purposes. Our methodology has three components: dimension reduction through a smooth factor model, time series modeling and forecasting of the factor scores, and dynamic updating using penalized least squares. The proposed methods are illustrated via the motivating application and two simulation studies. © 2009 American Statistical Association.-
dc.languageeng-
dc.relation.ispartofTechnometrics-
dc.subjectDimension reduction-
dc.subjectFactor models-
dc.subjectFunctional data analysis-
dc.subjectPenalization-
dc.subjectShrinkage-
dc.subjectSingular value decomposition-
dc.titleOn modeling and forecasting time series of smooth curves-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1198/tech.2009.08100-
dc.identifier.scopuseid_2-s2.0-69249132941-
dc.identifier.volume51-
dc.identifier.issue3-
dc.identifier.spage227-
dc.identifier.epage238-

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