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    Rights statement: 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

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

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The impact of special days in call arrivals forecasting : a neural network approach to modelling special days. / Barrow, Devon Kennard; Kourentzes, Nikolaos.

In: European Journal of Operational Research, Vol. 264, No. 3, 01.02.2018, p. 967-977.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Barrow DK, Kourentzes N. The impact of special days in call arrivals forecasting: a neural network approach to modelling special days. European Journal of Operational Research. 2018 Feb 1;264(3):967-977. Epub 2016 Jul 14. doi: 10.1016/j.ejor.2016.07.015

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Bibtex

@article{e31ae45945714fc69e7e704dbc8eb040,
title = "The impact of special days in call arrivals forecasting: a neural network approach to modelling special days",
abstract = "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.",
keywords = "Time Series Forecasting, Call Centre, Outliers, Functional Data, Neural Networks",
author = "Barrow, {Devon Kennard} and Nikolaos Kourentzes",
note = "This is the author{\textquoteright}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",
year = "2018",
month = feb,
day = "1",
doi = "10.1016/j.ejor.2016.07.015",
language = "English",
volume = "264",
pages = "967--977",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

TY - JOUR

T1 - The impact of special days in call arrivals forecasting

T2 - a neural network approach to modelling special days

AU - Barrow, Devon Kennard

AU - Kourentzes, Nikolaos

N1 - 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

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

KW - Time Series Forecasting

KW - Call Centre

KW - Outliers

KW - Functional Data

KW - Neural Networks

U2 - 10.1016/j.ejor.2016.07.015

DO - 10.1016/j.ejor.2016.07.015

M3 - Journal article

VL - 264

SP - 967

EP - 977

JO - European Journal of Operational Research

JF - European Journal of Operational Research

SN - 0377-2217

IS - 3

ER -