Forecasting intraday call arrivals using the seasonal moving average method

Devon K. Barrow

Research output: Contribution to journalArticle

17 Citations (Scopus)
47 Downloads (Pure)

Abstract

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.

Publisher Statement: NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Business 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 Journal of Business Research, [69, 12, (2017)] DOI: 10.1016/j.jbusres.2016.06.016

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Original languageEnglish
Pages (from-to)6088-6096
Number of pages9
JournalJournal of Business Research
Volume69
Issue number12
Early online date25 Jul 2016
DOIs
Publication statusPublished - Dec 2016

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Business 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 Journal of Business Research, [69, 12, (2017)] DOI: 10.1016/j.jbusres.2016.06.016

© 2017, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Keywords

  • Call center arrivals
  • Time series forecasting
  • Seasonal average
  • Univariate methods
  • Artificial neural networks
  • Forecast combination

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