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  • Reproducibility IJF Boylan et al

    Rights statement: This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 31, 2015 DOI: 10.1016/j.ijforecast.2014.05.008

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    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Reproducibility in forecasting research

Research output: Contribution to journalJournal articlepeer-review

Published
  • John Boylan
  • Paul Goodwin
  • Maryam Mohammadipour
  • Aris Syntetos
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<mark>Journal publication date</mark>01/2015
<mark>Journal</mark>International Journal of Forecasting
Issue number1
Volume31
Number of pages12
Pages (from-to)79-90
Publication StatusPublished
Early online date7/11/14
<mark>Original language</mark>English

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 been insufficiently specified, 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, IJF). The teams of researchers proceeded systematically, reporting results 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 & Williams. These discrepancies led to differences in the conclusions on 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, more generally, in its approach to the reproduction of forecasting research.

Bibliographic note

This is the author’s version of a work that was accepted for publication in International Journal of Forecasting. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International Journal of Forecasting, 31, 2015 DOI: 10.1016/j.ijforecast.2014.05.008