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June 2008 Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management
Haipeng Shen, Jianhua Z. Huang
Ann. Appl. Stat. 2(2): 601-623 (June 2008). DOI: 10.1214/08-AOAS164

Abstract

We 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.

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Haipeng Shen. Jianhua Z. Huang. "Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management." Ann. Appl. Stat. 2 (2) 601 - 623, June 2008. https://doi.org/10.1214/08-AOAS164

Information

Published: June 2008
First available in Project Euclid: 3 July 2008

zbMATH: 05591290
MathSciNet: MR2524348
Digital Object Identifier: 10.1214/08-AOAS164

Keywords: Dimension reduction , factor model , forecast updating , penalized likelihood , queueing systems , service engineering , Singular value decomposition , vector time series

Rights: Copyright © 2008 Institute of Mathematical Statistics

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Vol.2 • No. 2 • June 2008
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