Nonparametric time series prediction:: A semi-functional partial linear modeling
Nonparametric time series prediction:: A semi-functional partial linear modeling
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DOI：
10.1016/j.jmva.2007.04.010
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发表时间：
2008-05-01
影响因子：
1.6
通讯作者：
Vieu, Philippe
中科院分区：
文献类型：
--
作者：
Aneiros-Perez, German;Vieu, Philippe
最近人们对开发新的统计方法感兴趣，通过考虑过去值的连续集合作为预测因子来预测时间序列。在这种函数时间序列预测方法中，我们提出了部分线性模型的函数版本，它允许考虑额外的协变量，并使用过去的连续路径来预测过程的未来值。本文的目的是提出这个模型，构造一些估计，并从理论的角度通过渐近结果和从实际的角度通过处理一些实际数据集来观察它们的性质。虽然关于使用参数或非参数泛函建模的文献越来越多，但据我们所知，这是第一篇关于半参数泛函建模用于时间序列预测的论文。(c) 2007爱思唯尔公司版权所有。
There is a recent interest in developing new statistical methods to predict time series by taking into account a continuous set of past values as predictors. In this functional time series prediction approach, we propose a functional version of the partial linear model that allows both to consider additional covariates and to use a continuous path in the past to predict future values of the process. The aim of this paper is to present this model, to construct some estimates and to look at their properties both from a theoretical point of view by means of asymptotic results and from a practical perspective by treating some real data sets. Although the literature on the use of parametric or nonparametric functional modeling is growing, as far as we know, this is the first paper on semiparametric functional modeling for the prediction of time series. (c) 2007 Elsevier Inc. All rights reserved.