Publication: Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting
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2008-03
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Abstract
Year-ahead forecasting of electricity prices is an important issue in the current context of
electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in
previous published works. Moreover, methodology developed for the short-term does not work
properly for long-term forecasting.
In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis,
to deal with the interesting problem (both from the economic and engineering point of view) of
long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows
to deal with dimensionality reduction in vectors of time series, in such a way that extracts
common and specific components. Furthermore, common factors are able to capture not only
regular dynamics (stationary or not) but also seasonal one, by means of common factors
following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model.
Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters
involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due
to parameter estimation, allowing to enhance the coverage of forecast confidence intervals.
Concerning the innovative and challenging application provided, bootstrap procedure developed
allows to calculate not only point forecasts but also forecasting intervals for electricity prices.
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Keywords
Dynamic factor analysis, Bootstrap, Forecasting, Confidence intervals