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
Empirical Economics – Springer Journals
Published: Jun 2, 2018
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