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The impact of special days in call arrivals forecasting: a neural network approach to modelling special days

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>1/02/2018
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Number of pages11
Pages (from-to)967-977
Early online date14/07/16
<mark>Original language</mark>English


A key challenge for call centres remains the forecasting of high frequency call arrivals collected in hourly or shorter time buckets. In addition to the complex intraday, intraweek and intrayear seasonal cycles, call arrival data typically contain a large number of anomalous days, driven by the occurrence of holidays, special events, promotional activities and system failures. This study evaluates the use of a variety of univariate time series forecasting methods for forecasting intraday call arrivals in the presence of such outliers. Apart from established statistical methods we consider artificial neural networks (ANNs). Based on the modelling flexibility of the latter we introduce and evaluate different methods to encode the outlying periods. Using intraday arrival series from a call centre operated by one of Europe's leading entertainment companies, we provide new insights on the impact of outliers on the performance of established forecasting methods. Results show that ANNs forecast call centre data accurately, and are capable of modelling complex outliers using relatively simple outlier modelling approaches. We argue that the relative complexity of ANNs over standard statistical models is offset by the simplicity of coding multiple and unknown effects during outlying periods with ease.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European Journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 264 3, 2018 DOI: 10.1016/j.ejor.2016.07.015