Data transforms with exponential smoothing methods of forecasting |
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| Institution: | 1. Institute of Chemical Process Engineering, Boeblinger Str. 78, 70199 Stuttgart, Germany;2. Department of Mathematics, P.O. Box 80010, 3508 TA Utrecht, The Netherlands;1. Graduate School of Convergence Science and Technology, Seoul National University, Republic of Korea;2. School of Electrical Engineering and Computer Science, ASRI, Seoul National University, Republic of Korea |
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| 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. |
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| Keywords: | State space models Performance measures ANOVA Maximum likelihood AIC |
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