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
Bastianin, A., Galeotti, M., Manera, M. (2019). Statistical and economic evaluation of time series models for forecasting arrivals at call centers. EMPIRICAL ECONOMICS, 57(3), 923-955 [10.1007/s00181-018-1475-y].
Statistical and economic evaluation of time series models for forecasting arrivals at call centers
Bastianin, A
;Manera, M
2019
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 arrivalsFile | Dimensione | Formato | |
---|---|---|---|
Bastianin-2019-Empirical Econ-VoR.pdf
Solo gestori archivio
Tipologia di allegato:
Publisher’s Version (Version of Record, VoR)
Licenza:
Tutti i diritti riservati
Dimensione
692.83 kB
Formato
Adobe PDF
|
692.83 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.