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

Statistical and economic evaluation of time series models for forecasting arrivals at call centers Empir Econ https://doi.org/10.1007/s00181-018-1475-y Statistical and economic evaluation of time series models for forecasting arrivals at call centers 1 2 Andrea Bastianin · Marzio Galeotti · Matteo Manera Received: 1 February 2016 / Accepted: 23 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Call centers’ managers are interested in obtaining accurate point and dis- tributional 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 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Empirical Economics Springer Journals

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

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Economics; Econometrics; Statistics for Business/Economics/Mathematical Finance/Insurance; Economic Theory/Quantitative Economics/Mathematical Methods
ISSN
0377-7332
eISSN
1435-8921
D.O.I.
10.1007/s00181-018-1475-y
Publisher site
See Article on Publisher Site

Abstract

Empir Econ https://doi.org/10.1007/s00181-018-1475-y Statistical and economic evaluation of time series models for forecasting arrivals at call centers 1 2 Andrea Bastianin · Marzio Galeotti · Matteo Manera Received: 1 February 2016 / Accepted: 23 April 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2018 Abstract Call centers’ managers are interested in obtaining accurate point and dis- tributional 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

Journal

Empirical EconomicsSpringer Journals

Published: Jun 2, 2018

References

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