Forecasting time series using principal component analysis with respect to instrumental variables

被引：8

作者：

Cornillon, P-A ^{[1
]}

Imam, W. ^{[2
]}

Matzner-Lober, E. ^{[1
]}

机构：

[1] Univ Rennes 2, Equipe Stat, IRMAR, UMR 6625, F-35043 Rennes, France

[2] Higher Inst Demog Studies & Res, Damascus, Syria

来源：

COMPUTATIONAL STATISTICS & DATA ANALYSIS | 2008年 / 52卷 / 03期

关键词：

additive spline; forecasting; PCAIV; time series;

D O I：

10.1016/j.csda.2007.06.017

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Two new forecasting methods of time series are introduced. They are both based on a factorial analysis method called spline principal component analysis with respect to instrumental variables (spline PCAIV). The first method is a straightforward application of spline PCAIV while the second one is an adaptation of spline PCAIV In the modified version, the used criteria according to the unknown value that need to be predicted are differentiated. Those two forecasting methods are shown to be well adapted to time series. (c) 2007 Elsevier B.V All rights reserved.

引用

页码：1269 / 1280

页数：12

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