报告问题：Forecasting in an inhomogeneous Poisson process, with applications to call center arrival data
报告人：Prof.Lawrence D. Brown
Statistics Department, University of Pennsylvania
Guanghua School, Beijing University
报告时间：2005年12月 28日下昼4：30-6：00
A call center is a centralized hub where customer and other telephone calls are dealt with by an organization. In today's economy, they have become the primary point of contact between customers and businesses. Accurate prediction of the call arrival rate is therefore indispensable for call center practitioners to staff their call center effciently and cost effectively. This article proposes a multiplicative model for modeling and forecasting within-day arrival rates to a US commercial bank's call center. Markov chain Monte Carlo sampling methods are used to estimate both latent states and model parameters. One-day-ahead density forecasts for the rates and counts are provided. The calibration of these predictive distributions is evaluated through probability integral transforms. Furthermore, one-day-ahead forecasts comparisons with existing industry standards are given. Our predictions show significant improvements of up to 25% over these standards. A within-day learning algorithm is also proposed for sequential estimation and forecasts of the model parameters and rates.
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