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

Abstract

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. ARIMAmodels, 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.