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Reproducibility in forecasting research

Author

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  • Boylan, John E.
  • Goodwin, Paul
  • Mohammadipour, Maryam
  • Syntetos, Aris A.

Abstract

The importance of replication has been recognised across many scientific disciplines. Reproducibility is a necessary condition for replicability, because an inability to reproduce results implies that the methods have not been specified sufficiently, thus precluding replication. This paper describes how two independent teams of researchers attempted to reproduce the empirical findings of an important paper, “Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy” (Miller & Williams, 2003). The two teams proceeded systematically, reporting results both before and after receiving clarifications from the authors of the original study. The teams were able to approximately reproduce each other’s results, but not those of Miller and Williams. These discrepancies led to differences in the conclusions as to the conditions under which seasonal damping outperforms classical decomposition. The paper specifies the forecasting methods employed using a flowchart. It is argued that this approach to method documentation is complementary to the provision of computer code, as it is accessible to a broader audience of forecasting practitioners and researchers. The significance of this research lies not only in its lessons for seasonal forecasting but also, more generally, in its approach to the reproduction of forecasting research.

Suggested Citation

  • Boylan, John E. & Goodwin, Paul & Mohammadipour, Maryam & Syntetos, Aris A., 2015. "Reproducibility in forecasting research," International Journal of Forecasting, Elsevier, vol. 31(1), pages 79-90.
  • Handle: RePEc:eee:intfor:v:31:y:2015:i:1:p:79-90
    DOI: 10.1016/j.ijforecast.2014.05.008
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    References listed on IDEAS

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    1. McCullough, B. D., 2000. "Is it safe to assume that software is accurate?," International Journal of Forecasting, Elsevier, vol. 16(3), pages 349-357.
    2. De Gooijer, Jan G. & Hyndman, Rob J., 2006. "25 years of time series forecasting," International Journal of Forecasting, Elsevier, vol. 22(3), pages 443-473.
    3. McCullough, B.D. & Wilson, Berry, 2005. "On the accuracy of statistical procedures in Microsoft Excel 2003," Computational Statistics & Data Analysis, Elsevier, vol. 49(4), pages 1244-1252, June.
    4. Evanschitzky, Heiner & Armstrong, J. Scott, 2010. "Replications of forecasting research," International Journal of Forecasting, Elsevier, vol. 26(1), pages 4-8, January.
    5. Simmons, LeRoy F., 1986. "M-competition -- A closer look at NAIVE2 and median APE : A note," International Journal of Forecasting, Elsevier, vol. 2(4), pages 457-459.
    6. Miller, Don M. & Williams, Dan, 2003. "Shrinkage estimators of time series seasonal factors and their effect on forecasting accuracy," International Journal of Forecasting, Elsevier, vol. 19(4), pages 669-684.
    7. Evanschitzky, Heiner & Baumgarth, Carsten & Hubbard, Raymond & Armstrong, J. Scott, 2007. "Replication research's disturbing trend," Journal of Business Research, Elsevier, vol. 60(4), pages 411-415, April.
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    RePEc Biblio mentions

    As found on the RePEc Biblio, the curated bibliography for Economics:
    1. > Economics Profession > Publishing in Economics > Replication

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    Cited by:

    1. Hyndman, Rob J., 2020. "A brief history of forecasting competitions," International Journal of Forecasting, Elsevier, vol. 36(1), pages 7-14.
    2. Schaer, Oliver & Kourentzes, Nikolaos & Fildes, Robert, 2019. "Demand forecasting with user-generated online information," International Journal of Forecasting, Elsevier, vol. 35(1), pages 197-212.
    3. Hu, Qiwei & Boylan, John E. & Chen, Huijing & Labib, Ashraf, 2018. "OR in spare parts management: A review," European Journal of Operational Research, Elsevier, vol. 266(2), pages 395-414.
    4. Fiorucci, Jose A. & Pellegrini, Tiago R. & Louzada, Francisco & Petropoulos, Fotios & Koehler, Anne B., 2016. "Models for optimising the theta method and their relationship to state space models," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1151-1161.
    5. Petropoulos, Fotios & Wang, Xun & Disney, Stephen M., 2019. "The inventory performance of forecasting methods: Evidence from the M3 competition data," International Journal of Forecasting, Elsevier, vol. 35(1), pages 251-265.
    6. Trapero, Juan R. & Kourentzes, Nikolaos & Martin, A., 2015. "Short-term solar irradiation forecasting based on Dynamic Harmonic Regression," Energy, Elsevier, vol. 84(C), pages 289-295.

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