The Annals of Applied Statistics

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

Haipeng Shen and Jianhua Z. Huang

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 2, Number 2 (2008), 601-623.

Dates
First available in Project Euclid: 3 July 2008

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1215118530

Digital Object Identifier
doi:10.1214/08-AOAS164

Mathematical Reviews number (MathSciNet)
MR2524348

Zentralblatt MATH identifier
05591290

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

Citation

Shen, Haipeng; Huang, Jianhua Z. Forecasting time series of inhomogeneous Poisson processes with application to call center workforce management. Ann. Appl. Stat. 2 (2008), no. 2, 601--623. doi:10.1214/08-AOAS164. https://projecteuclid.org/euclid.aoas/1215118530


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