The Prediction of Time Series With Trends and Seasonalities
利用赤池信息准则（AIC）的最大化预测分布期望熵解释，对具有趋势和季节性的时间序列进行建模和预测，发现一步和十二步最优预测模型不同，并讨论了与最优趋势估计和季节调整的关系。
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.