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. 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.
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Schneider, C., Arminger, G. (2007). Factor Analysis for Extraction of Structural Components and Prediction in Time Series. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_31
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DOI: https://doi.org/10.1007/978-3-540-70981-7_31
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