Factor analysis for extraction of structural components and prediction in time series

被引：0
|
作者：
Schneider, Carsten [1 ]
Arminger, Gerhard [1 ]
机构：
[1] Berg Univ Wuppertal, Fachbereich 9, D-42097 Wuppertal, Germany
来源：
ADVANCES IN DATA ANALYSIS | 2007年
关键词：
D O I：
10.1007/978-3-540-70981-7_31
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, factor analysis is used for the dimensional reduction of complex time series. If the structure within data is too complex to use e.g. ARIMA-models, factor analysis can be used for simplification without relevant loss of explained variation. The result are data with simple structure that can be forecasted by a standard prediction model. To give an example for this approach we predict the electricity demand per quarter of an hour of industrial customers in Germany. The data have a rather complex structure with 96 observations per day and possibly different cyclical variations during the day regarding different weekdays.
引用
收藏
页码：273 / +
页数：2
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