JSM 2005 Online Program

Abstract #303993

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	16
Type:	Topic Contributed
Date/Time:	Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor:	Section on Bayesian Statistical Science
Abstract - #303993
Title:	A Bayesian Approach to Modeling an Inhomogeneous Poisson Process with Applications to Call Center Data
Author(s):	Jonathan Weinberg*+
Companies:	University of Pennsylvania
Address:	2101 Chestnut Street, Philadelphia, PA, 19103, United States
Keywords:	Autoregressive models ; Cubic Smoothing Spline ; Poisson Process
Abstract:	A call center is a physical place where customer and other telephone calls are dealt with by an organization, usually with some amount of computer automation. Customers wishing to speak to a service agent are placed in a queue until the next agent becomes available. Call arrivals to the queue approximately follow an inhomogeneous Poisson process. A model for forecasting the Poisson arrival rates is proposed in this paper. The rates are modeled as a stochastic process, which is a function of the day of the week, time of day, and other covariates. Markov chain Monte Carlo methods based on the Gibbs sampler and the Metropolis Hastings algorithm are used to sample from the joint posterior distribution of the latent states and model parameters. Careful consideration is placed on the choice of priors. We apply our approach to data from a large commercial bank based in the United States. The data consist of call volumes from March through October of 2003. Our approach is extremely effective in predicting the daily pattern of arrivals one day ahead and outperforms existing methods.

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