G2TT
来源类型FEEM working papers "Note di lavoro" series
规范类型论文
Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers
Andrea Bastianin; Marzio Galeotti; Matteo Manera
发表日期2017
出处Economic Theory
出版年2017
语种英语
摘要Call centers' managers are interested in obtaining accurate forecasts of call arrivals because these are a key input in staffing and scheduling decisions. Therefore their ability to achieve an optimal balance between service quality and operating costs ultimately hinges on forecast accuracy. We present a strategy to model selection in call centers which is based on three pillars: (i) a flexible loss function; (ii) statistical evaluation of forecast accuracy; (iii) economic evaluation of forecast performance using money metrics. We implement fourteen time series models and seven forecast combination schemes on three series of call arrivals. We show that second moment modeling is important when forecasting call arrivals. From the point of view of a call center manager, our results indicate that outsourcing the development of a forecasting model is worth its cost, since the simple Seasonal Random Walk model is always outperformed by other, relatively more sophisticated, specifications. *** Suggested citation: Bastianin, A., M. Galeotti, M. Manera, (2017), 'Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers', Nota di Lavoro 6.2017, Milan, Italy: Fondazione Eni Enrico Mattei
特色分类C22;C25;C53;D81;M15
关键词ARIMA Call Center Arrivals Loss Function Seasonality Telecommunications Forecasting
URLhttps://www.feem.it/en/publications/feem-working-papers-note-di-lavoro-series/statistical-and-economic-evaluation-of-time-series-models-for-forecasting-arrivals-at-call-centers/
来源智库Fondazione Eni Enrico Mattei (Italy)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/118523
推荐引用方式
GB/T 7714
Andrea Bastianin,Marzio Galeotti,Matteo Manera. Statistical and Economic Evaluation of Time Series Models for Forecasting Arrivals at Call Centers. 2017.
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