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Minimum Sample Size requirements for Seasonal Forecasting Models

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  • Rob J. Hyndman
  • Andrey V. Kostenko

Abstract

Authors Rob Hyndman and Andrey Kostenko discuss the bare minimum data requirements for fitting three common types of seasonal models: regression with seasonal dummies, exponential smoothing, and ARIMA. Achieving the requisite minimum numbers, however, does not ensure adequate estimates of seasonality. The amount of additional data required depends on the amount of noise (random variation) in the data. Unfortunately, there are no simple rules about sample size, and the authors note that published tables on sample size requirements are overly simplified. Copyright International Institute of Forecasters, 2007

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Bibliographic Info

Article provided by International Institute of Forecasters in its journal Foresight: The International Journal of Applied Forecasting.

Volume (Year): (2007)
Issue (Month): 6 (Spring)
Pages: 12-15

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Handle: RePEc:for:ijafaa:y:2007:i:6:p:12-15

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Cited by:
  1. Rob J. Hyndman & Yeasmin Khandakar, 2007. "Automatic time series forecasting: the forecast package for R," Monash Econometrics and Business Statistics Working Papers 6/07, Monash University, Department of Econometrics and Business Statistics.
  2. Kolassa, Stephan, 2011. "Combining exponential smoothing forecasts using Akaike weights," International Journal of Forecasting, Elsevier, vol. 27(2), pages 238-251, April.

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