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 arrivals
Articolo in rivista - Articolo scientifico
ARIMA; Call center arrivals; Loss function; Seasonality; Telecommunications forecasting; Statistics and Probability; Mathematics (miscellaneous); Social Sciences (miscellaneous); Economics and Econometrics
English
2-giu-2018
2019
57
3
923
955
reserved
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].
File in questo prodotto:
File 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.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/204720
Citazioni
  • Scopus 1
  • ???jsp.display-item.citation.isi??? 0
Social impact