An approach to the prediction of time series with trends and seasonalities
An approach to the prediction of time series with trends and seasonalities
复制标题
预测具有趋势和季节性的时间序列的方法
DOI：
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发表时间：
1982
期刊：
影响因子：
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通讯作者：
G. Kitagawa
中科院分区：
文献类型：
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作者：
W. Gersch;G. Kitagawa
从赤池最小AIC过程的预测分布解释的期望熵的最大化接近趋势和季节均值函数和平稳协方差的时间序列的建模和预测。AIC准则最佳一步预测模型和最佳十二步预测模型是不同的。他们表现出相对的最优性能，他们的设计。研究结果与时间序列的最优趋势估计和最优季节调整的开放性问题有关。
The modeling and prediction of time series with trend and seasonal mean value functions and stationary covariances is approached from a maximization of the expected entropy of the predictive distribution interpretation of Akaike's minimum AIC procedure. The AIC criterion best one-step-ahead and best twelvestep-ahead prediction models are different. They 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.