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Activity Number:
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38
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #307765 |
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Title:
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Bayesian Forecasting of an Inhomogeneous Poisson Process with Applications to Call Center Data
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Author(s):
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Jonathan Stroud*+ and Jonathan Weinberg and Lawrence D. Brown
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Companies:
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University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
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Address:
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Wharton School, Philadelphia, PA, 19104,
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Keywords:
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Autoregressive models ; Bayesian forecasting ; Call centers ; Cubic smoothing spline ; Poisson process ; Sequential Monte Carlo
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Abstract:
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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 efficiently 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 and sequential Monte Carlo methods are used to estimate both latent states and model parameters. The calibration of these predictive densities is evaluated through probability integral transforms. Furthermore, we provide one-day-ahead forecast comparisons with classical statistical models. Our predictions show improvements of up to 25% over these standards.
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