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Old dog, new tricks: a modelling view of simple moving averages

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  • Ivan Svetunkov
  • Fotios Petropoulos

Abstract

Simple moving average (SMA) is a well-known forecasting method. It is easy to understand and interpret and easy to use, but it does not have an appropriate length selection mechanism and does not have an underlying statistical model. In this paper, we show two statistical models underlying SMA and demonstrate that the automatic selection of the optimal length of the model can easily be done using this finding. We then evaluate the proposed model on a real data-set and compare its performance with other popular simple forecasting methods. We find that SMA performs better both in terms of point forecasts and prediction intervals in cases of normal and cumulative values.

Suggested Citation

  • Ivan Svetunkov & Fotios Petropoulos, 2018. "Old dog, new tricks: a modelling view of simple moving averages," International Journal of Production Research, Taylor & Francis Journals, vol. 56(18), pages 6034-6047, September.
    • Handle: RePEc:taf:tprsxx:v:56:y:2018:i:18:p:6034-6047
    DOI: 10.1080/00207543.2017.1380326
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    Cited by:

    1. Spiliotis, Evangelos & Assimakopoulos, Vassilios & Makridakis, Spyros, 2020. "Generalizing the Theta method for automatic forecasting," European Journal of Operational Research, Elsevier, vol. 284(2), pages 550-558.
    2. Kang, Yanfei & Spiliotis, Evangelos & Petropoulos, Fotios & Athiniotis, Nikolaos & Li, Feng & Assimakopoulos, Vassilios, 2021. "Déjà vu: A data-centric forecasting approach through time series cross-similarity," Journal of Business Research, Elsevier, vol. 132(C), pages 719-731.
    3. Kang, Yanfei & Cao, Wei & Petropoulos, Fotios & Li, Feng, 2022. "Forecast with forecasts: Diversity matters," European Journal of Operational Research, Elsevier, vol. 301(1), pages 180-190.
    4. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios & Chen, Zhi & Gaba, Anil & Tsetlin, Ilia & Winkler, Robert L., 2022. "The M5 uncertainty competition: Results, findings and conclusions," International Journal of Forecasting, Elsevier, vol. 38(4), pages 1365-1385.
    5. Omar, Haytham & Klibi, Walid & Babai, M. Zied & Ducq, Yves, 2023. "Basket data-driven approach for omnichannel demand forecasting," International Journal of Production Economics, Elsevier, vol. 257(C).
    6. Deimante Teresiene & Margarita Aleksynaite, 2020. "The Use of Technical Analysis in the US, European and Asian Stock Markets," Technium Social Sciences Journal, Technium Science, vol. 8(1), pages 302-318, June.
    7. Fotios Petropoulos & Enno Siemsen, 2023. "Forecast Selection and Representativeness," Management Science, INFORMS, vol. 69(5), pages 2672-2690, May.
    8. Mavhura, Emmanuel & Raj Aryal, Komal, 2023. "Disaster mortalities and the Sendai Framework Target A: Insights from Zimbabwe," World Development, Elsevier, vol. 165(C).
    9. Evangelos Spiliotis & Spyros Makridakis & Artemios-Anargyros Semenoglou & Vassilios Assimakopoulos, 2022. "Comparison of statistical and machine learning methods for daily SKU demand forecasting," Operational Research, Springer, vol. 22(3), pages 3037-3061, July.
    10. Christos Spandonidis & Dimitrios Paraskevopoulos & Christina Saravanos, 2023. "Neighborhood-Level Particle Pollution Assessment during the COVID-19 Pandemic via a Novel IoT Solution," Sustainability, MDPI, vol. 15(10), pages 1-16, May.
    11. Thi-Nham Le & Thanh-Tuan Dang, 2022. "An Integrated Approach for Evaluating the Efficiency of FDI Attractiveness: Evidence from Vietnamese Provincial Data from 2012 to 2022," Sustainability, MDPI, vol. 14(20), pages 1-25, October.
    12. repec:thr:techub:1008:y:2020:i:1:p:302-318 is not listed on IDEAS

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