Long-Term Prediction Intervals of Time Series

被引：17
|
作者：
Zhou, Zhou [1 ]
Xu, Zhiwei [3 ]
Wu, Wei Biao [2 ]
机构：
[1] Univ Toronto, Dept Stat, Toronto, ON M5S 3G3, Canada
[2] Univ Chicago, Dept Stat, Chicago, IL 60637 USA
[3] Univ Michigan, Dept Comp & Informat Sci, Dearborn, MI 48128 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2010年 / 56卷 / 03期
关键词：
Empirical quantiles; heavy tails; long-memory; long-run prediction; quenched central limit theory; SEQUENTIAL PREDICTION; UNIVERSAL SCHEMES;
D O I：
10.1109/TIT.2009.2039158
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider the problem of predicting aggregates or sums of future values of a process based on its past values. In contrast with the conventional prediction problem in which one predicts a future value given past values of the process, in our setting the number of aggregates can go to infinity with respect to the number of available observations. Consistency and Bahadur representations of the prediction estimators are established. A simulation study is carried out to assess the performance of different prediction estimators.
引用
收藏
页码：1436 / 1446
页数：11
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