Journal article

Data transforms with exponential smoothing methods of forecasting

Adrian N Beaumont

INTERNATIONAL JOURNAL OF FORECASTING | ELSEVIER | Published : 2014

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. © 2014 International Institute of Forecasters.

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