Sales revenues: Time‐series properties and predictions

A. R. Abdel‐Khalik, K. M. El‐Sheshai

Research output: Contribution to journalArticlepeer-review


This paper compares the predictive ability of ARIMA models in forecasting sales revenue. Comparisons were made at both industry and firm levels. With respect to the form of the ARIMA model, a parsimonious model of the form (0, 1, 1) (0, 1, 1) was identified most frequently for firms and industries. This model was identified previously by Griffin and Watts for the earnings series, and by Moriarty and Adams for the sales series. As a parsimonious model, its predictive accuracy was quite good. However, predictive accuracy was also found to be a function of the industry. Out of the eleven industry classifications, ‘metals’ had the lowest predictive accuracy using both firmspecific and industry‐specific ARIMA models.

Original languageEnglish (US)
Pages (from-to)351-362
Number of pages12
JournalJournal of Forecasting
Issue number4
StatePublished - 1983


  • ARIMA application
  • Forecastin
  • Parsimonious models
  • Predictive accuracy
  • Sales

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Strategy and Management
  • Statistics, Probability and Uncertainty
  • Management Science and Operations Research


Dive into the research topics of 'Sales revenues: Time‐series properties and predictions'. Together they form a unique fingerprint.

Cite this