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ARIMA: The Models of Box and Jenkins

Author

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  • Eric Stellwagen
  • Len Tashman

Abstract

Foresight tutorials are designed to be nontechnical overviews of important methodologies, enabling business forecasters to make more informed use of their forecasting software. The Fall 2012 issue contained Eric StellwagenÕs tutorial ÒExponential Smoothing: The Workhorse of Business Forecasting.Ó Eric and Len now team up to discuss ARIMA, the models popularized by Box and Jenkins. They examine the pros and cons of ARIMA modeling, provide a conceptual overview of how the technique works, and discuss how best to apply it to business data. Copyright International Institute of Forecasters, 2013

Suggested Citation

  • Eric Stellwagen & Len Tashman, 2013. "ARIMA: The Models of Box and Jenkins," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 30, pages 28-33, Summer.
    • Handle: RePEc:for:ijafaa:y:2013:i:29:p:28-33
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. What I Learned in Turkey About Forecasting Turkey Dinner
      by realizingresonance@gmail.com (Jared Endicott) in Realizing Resonance on 2013-11-25 15:25:00

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    Cited by:

    1. Fady M. A Hassouna & Khaled Al-Sahili, 2020. "Environmental Impact Assessment of the Transportation Sector and Hybrid Vehicle Implications in Palestine," Sustainability, MDPI, vol. 12(19), pages 1-12, September.
    2. Han-Khanh Nguyen, 2020. "Combining DEA and ARIMA Models for Partner Selection in the Supply Chain of Vietnam’s Construction Industry," Mathematics, MDPI, vol. 8(6), pages 1-20, May.
    3. Haicheng Ling & Pierre-Yves Massé & Thibault Rihet & Frédéric Wurtz, 2023. "Realistic Nudging through ICT Pipelines to Help Improve Energy Self-Consumption for Management in Energy Communities," Energies, MDPI, vol. 16(13), pages 1-24, July.
    4. Elalem, Yara Kayyali & Maier, Sebastian & Seifert, Ralf W., 2023. "A machine learning-based framework for forecasting sales of new products with short life cycles using deep neural networks," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1874-1894.

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