Forecasting transformed series
Forecasting transformed series
复制标题
预测变换序列
DOI：
10.1017/cbo9780511753961.024
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发表时间：
1976
期刊：
影响因子：
--
通讯作者：
P. Newbold
中科院分区：
文献类型：
--
作者：
C. Granger;P. Newbold
假设预测模型可用于过程 Xt，但兴趣集中在瞬时变换 Yt = T(Xt) 上。假设 Xt 是高斯且平稳的，或者可以通过差分降低到平稳性，本文研究了变换序列的自协方差结构和预测方法。该开发采用 Hermite 多项式展开，从而可以导出非常通用的瞬时变换类的结果。
Suppose that a forecasting model is available for the process Xt but that interest centres on the instantaneous transformation Yt = T(Xt). On the assumption that Xt is Gaussian and stationary, or can be reduced to stationarity by differencing, this paper examines the autocovariance structure of and methods for forecasting the transformed series. The development employs the Hermite polynomial expansion, thus allowing results to be derived for a very general class of instantaneous transformations.