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Activity Number:
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498
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Type:
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Invited
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Date/Time:
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Thursday, August 7, 2008 : 10:30 AM to 12:20 PM
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Sponsor:
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WNAR
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| Abstract - #300291 |
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Title:
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Forecasting Time Series of Inhomogeneous Poisson Processes
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Author(s):
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Jianhua Huang*+ and Haipeng Shen
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Companies:
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Texas A&M University and The University of North Carolina at Chapel Hill
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Address:
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Department of Statistics, College Station, TX, 77843,
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Keywords:
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dimension reduction ; factor model ; penalized likelihood ; queueing systems ; service engineering ; vector time series
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Abstract:
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We consider forecasting the latent and uncertain rate profiles of a time series of inhomogeneous Poisson processes. The work is motivated by operation management of queuing systems such as call 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. In particular ,we show how forecasting and dynamic updating of call arrival rates can affect the accuracy of call center staffing.
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