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The Prediction of Time Series with Trends and Seasonalities
Will Gersch and Genshiro Kitagawa
Journal of Business & Economic Statistics
Vol. 1, No. 3 (Jul., 1983), pp. 253-264
Stable URL: http://www.jstor.org/stable/1391347
Page Count: 12
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A maximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure is exploited for the modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances. The AIC criterion best one-step-ahead and best twelve-step-ahead prediction models can be different. The different models exhibit the relative optimality properties for which they were designed. The results are related to open questions on optimal trend estimation and optimal seasonal adjustment of time series.
Journal of Business & Economic Statistics © 1983 American Statistical Association