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A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition

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  • Pantelis Agathangelou

    (Department of Computer Science, School of Sciences and Engineering, University of Nicosia, Nicosia CY-2417, Cyprus)

  • Demetris Trihinas

    (Department of Computer Science, School of Sciences and Engineering, University of Nicosia, Nicosia CY-2417, Cyprus)

  • Ioannis Katakis

    (Department of Computer Science, School of Sciences and Engineering, University of Nicosia, Nicosia CY-2417, Cyprus)

Abstract

As forecasting becomes more and more appreciated in situations and activities of everyday life that involve prediction and risk assessment, more methods and solutions make their appearance in this exciting arena of uncertainty. However, less is known about what makes a promising or a poor forecast. In this article, we provide a multi-factor analysis on the forecasting methods that participated and stood out in the M4 competition, by focusing on Error (predictive performance), Correlation (among different methods), and Complexity (computational performance). The main goal of this study is to recognize the key elements of the contemporary forecasting methods, reveal what made them excel in the M4 competition, and eventually provide insights towards better understanding the forecasting task.

Suggested Citation

  • Pantelis Agathangelou & Demetris Trihinas & Ioannis Katakis, 2020. "A Multi-Factor Analysis of Forecasting Methods: A Study on the M4 Competition," Data, MDPI, vol. 5(2), pages 1-24, April.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:2:p:41-:d:349049
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    References listed on IDEAS

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    1. Smyl, Slawek, 2020. "A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting," International Journal of Forecasting, Elsevier, vol. 36(1), pages 75-85.
    2. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    3. Pawlikowski, Maciej & Chorowska, Agata, 2020. "Weighted ensemble of statistical models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 93-97.
    4. Montero-Manso, Pablo & Athanasopoulos, George & Hyndman, Rob J. & Talagala, Thiyanga S., 2020. "FFORMA: Feature-based forecast model averaging," International Journal of Forecasting, Elsevier, vol. 36(1), pages 86-92.
    5. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    6. Petropoulos, Fotios & Svetunkov, Ivan, 2020. "A simple combination of univariate models," International Journal of Forecasting, Elsevier, vol. 36(1), pages 110-115.
    7. Jaganathan, Srihari & Prakash, P.K.S., 2020. "A combination-based forecasting method for the M4-competition," International Journal of Forecasting, Elsevier, vol. 36(1), pages 98-104.
    8. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    9. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
    10. Holt, Charles C., 2004. "Author's retrospective on 'Forecasting seasonals and trends by exponentially weighted moving averages'," International Journal of Forecasting, Elsevier, vol. 20(1), pages 11-13.
    11. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2018. "The M4 Competition: Results, findings, conclusion and way forward," International Journal of Forecasting, Elsevier, vol. 34(4), pages 802-808.
    12. Shaub, David, 2020. "Fast and accurate yearly time series forecasting with forecast combinations," International Journal of Forecasting, Elsevier, vol. 36(1), pages 116-120.
    13. Holt, Charles C., 2004. "Forecasting seasonals and trends by exponentially weighted moving averages," International Journal of Forecasting, Elsevier, vol. 20(1), pages 5-10.
    14. Doornik, Jurgen A. & Castle, Jennifer L. & Hendry, David F., 2020. "Card forecasts for M4," International Journal of Forecasting, Elsevier, vol. 36(1), pages 129-134.
    15. Fiorucci, Jose Augusto & Louzada, Francisco, 2020. "GROEC: Combination method via Generalized Rolling Origin Evaluation," International Journal of Forecasting, Elsevier, vol. 36(1), pages 105-109.
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