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Empirical Economics

, Volume 57, Issue 3, pp 923–955 | Cite as

Statistical and economic evaluation of time series models for forecasting arrivals at call centers

  • Andrea BastianinEmail author
  • Marzio Galeotti
  • Matteo Manera
Article
  • 98 Downloads

Abstract

Call centers’ managers are interested in obtaining accurate point and distributional forecasts of call arrivals in order to achieve an optimal balance between service quality and operating costs. We present a strategy for selecting forecast models of call arrivals which is based on three pillars: (i) flexibility of the loss function; (ii) statistical evaluation of forecast accuracy; and (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of daily call arrivals. Although we focus mainly on point forecasts, we also analyze density forecast evaluation. We show that second-moment modeling is important for both point and density forecasting and that the simple seasonal random walk model is always outperformed by more general specifications. Our results suggest that call center managers should invest in the use of forecast models which describe both first and second moments of call arrivals.

Keywords

ARIMA Call center arrivals Loss function Seasonality Telecommunications forecasting 

JEL Classification

C22 C25 C53 D81 M15 

Notes

Acknowledgements

We are grateful to one anonymous referee and the editor, Robert Kunst, for constructive comments.

Supplementary material

181_2018_1475_MOESM1_ESM.pdf (559 kb)
Supplementary material 1 (pdf 559 KB)

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of MilanMilanItaly
  2. 2.University of MilanMilanItaly
  3. 3.University of Milan-BicoccaMilanItaly

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