Forecasting intraday call arrivals using the seasonal moving average method

被引:30
作者
Barrow, Devon K. [1 ]
机构
[1] Coventry Univ, Coventry Business Sch, Priory St, Coventry CV1 5FB, W Midlands, England
关键词
Call center arrivals; Time series forecasting; Seasonal average; Univariate methods; Artificial neural networks; Forecast combination; TIME-SERIES; NEURAL-NETWORKS; STATE; PREDICTION; DEMAND; MODELS; ART; DECOMPOSITION; PERSPECTIVE; MANAGEMENT;
D O I
10.1016/j.jbusres.2016.06.016
中图分类号
F [经济];
学科分类号
02 ;
摘要
Research into time series forecasting for call center management suggests that a forecast based on the simple Seasonal Moving Average (SMA) method outperforms more sophisticated approaches at long horizons where capacity planning decisions are made. However in the short to medium term where decisions concerning the scheduling of agents are required, the SMA method is usually outperformed. This study is the first systematic evaluation of the SMA method across averages of different lengths using call arrival data sampled at different frequencies from 5 min to 1 h. A hybrid method which combines the strengths of the SMA method and nonlinear data-driven artificial neural networks (ANNs) is proposed to improve short-term accuracy without deteriorating long-term performance. Results of forecasting the intraday call arrivals to banks in the US, UK and Israel indicate that the proposed method outperforms standard benchmarks, and leads to improvements in forecasting accuracy across all horizons. (C) 2016 Elsevier Inc. All rights reserved.
引用
收藏
页码:6088 / 6096
页数:9
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