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Forecasting Call Centre Arrivals
Author(s) -
Millán‒Ruiz David,
Hidalgo J. Ignacio
Publication year - 2013
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2258
Subject(s) - computer science , aggregate (composite) , artificial neural network , call management , volume (thermodynamics) , interval (graph theory) , econometrics , machine learning , call control , telecommunications , economics , mathematics , materials science , physics , quantum mechanics , combinatorics , composite material
ABSTRACT This article presents a novel neural network‒based approach to the intra‒day forecasting of call arrivals in call centres. We apply the method to individual time series of arrivals for different customer call groups. To train the model, we use historical call data from three months and, for each day, we aggregate the call volume in 288 intervals of 5 minutes. With these data, our method can be used for predicting the call volume in the next 5‒minute interval using either previous real data or previous predictions to iteratively produce multi‒step‒ahead forecasts. We compare our approach with other conventional forecasting techniques. Experimental results provide factual evidence in favour of our approach. Copyright © 2013 John Wiley & Sons, Ltd.