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Article: Interday forecasting and intraday updating of call center arrivals

TitleInterday forecasting and intraday updating of call center arrivals
Authors
KeywordsDynamic forecast updating
Vector time series
Penalized least squares
Principal component analysis
Singular value decomposition
Dimension reduction
Issue Date2008
Citation
Manufacturing and Service Operations Management, 2008, v. 10, n. 3, p. 391-410 How to Cite?
AbstractAccurate forecasting of call arrivals is critical for staffing and scheduling of a telephone call center. We develop methods for interday and dynamic intraday forecasting of incoming call volumes. Our approach is to treat the intraday call volume profiles as a high-dimensional vector time series. We propose first to reduce the dimensionality by singular value decomposition of the matrix of historical intraday profiles and then to apply time series and regression techniques. Our approach takes into account both interday (or day-to-day) dynamics and intraday (or within-day) patterns of call arrivals. Distributional forecasts are also developed. The proposed methods are data driven, appear to be robust against model assumptions in our simulation studies, and are shown to be very competitive in out-of-sample forecast comparisons using two real data sets. Our methods are computationally fast; it is therefore feasible to use them for real-time dynamic forecasting. © 2008 INFORMS.
Persistent Identifierhttp://hdl.handle.net/10722/219591
ISSN
2015 Impact Factor: 1.966
2015 SCImago Journal Rankings: 4.609

 

DC FieldValueLanguage
dc.contributor.authorShen, Haipeng-
dc.contributor.authorHuang, Jianhua Z.-
dc.date.accessioned2015-09-23T02:57:28Z-
dc.date.available2015-09-23T02:57:28Z-
dc.date.issued2008-
dc.identifier.citationManufacturing and Service Operations Management, 2008, v. 10, n. 3, p. 391-410-
dc.identifier.issn1523-4614-
dc.identifier.urihttp://hdl.handle.net/10722/219591-
dc.description.abstractAccurate forecasting of call arrivals is critical for staffing and scheduling of a telephone call center. We develop methods for interday and dynamic intraday forecasting of incoming call volumes. Our approach is to treat the intraday call volume profiles as a high-dimensional vector time series. We propose first to reduce the dimensionality by singular value decomposition of the matrix of historical intraday profiles and then to apply time series and regression techniques. Our approach takes into account both interday (or day-to-day) dynamics and intraday (or within-day) patterns of call arrivals. Distributional forecasts are also developed. The proposed methods are data driven, appear to be robust against model assumptions in our simulation studies, and are shown to be very competitive in out-of-sample forecast comparisons using two real data sets. Our methods are computationally fast; it is therefore feasible to use them for real-time dynamic forecasting. © 2008 INFORMS.-
dc.languageeng-
dc.relation.ispartofManufacturing and Service Operations Management-
dc.subjectDynamic forecast updating-
dc.subjectVector time series-
dc.subjectPenalized least squares-
dc.subjectPrincipal component analysis-
dc.subjectSingular value decomposition-
dc.subjectDimension reduction-
dc.titleInterday forecasting and intraday updating of call center arrivals-
dc.typeArticle-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1287/msom.1070.0179-
dc.identifier.scopuseid_2-s2.0-60849087198-
dc.identifier.volume10-
dc.identifier.issue3-
dc.identifier.spage391-
dc.identifier.epage410-
dc.identifier.eissn1526-5498-

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