Forecasting intraday call arrivals using the seasonal moving average method

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)

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.

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 - 1 Dec 2016

Keywords

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

ASJC Scopus subject areas

  • Marketing

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