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Data transforms with exponential smoothing methods of forecasting

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  • Beaumont, Adrian N.

Abstract

In this paper, transforms are used with exponential smoothing, in the quest for better forecasts. Two types of transforms are explored: those which are applied to a time series directly, and those which are applied indirectly to the prediction errors. The various transforms are tested on a large number of time series from the M3 competition, and ANOVA is applied to the results. We find that the non-transformed time series is significantly worse than some transforms on the monthly data, and on a distribution-based performance measure for both annual and quarterly data.

Suggested Citation

  • Beaumont, Adrian N., 2014. "Data transforms with exponential smoothing methods of forecasting," International Journal of Forecasting, Elsevier, vol. 30(4), pages 918-927.
  • Handle: RePEc:eee:intfor:v:30:y:2014:i:4:p:918-927
    DOI: 10.1016/j.ijforecast.2014.03.013
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    References listed on IDEAS

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    1. Makridakis, Spyros & Hibon, Michele, 2000. "The M3-Competition: results, conclusions and implications," International Journal of Forecasting, Elsevier, vol. 16(4), pages 451-476.
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    8. P. Frevert, 1971. "Note," Review of Economic Studies, Oxford University Press, vol. 38(2), pages 269-270.
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