Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

Abstract

Year-ahead forecasting of electricity prices is an important issue in the current context of
\nelectricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in
\nprevious published works. Moreover, methodology developed for the short-term does not work
\nproperly for long-term forecasting.
\nIn this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis,
\nto deal with the interesting problem (both from the economic and engineering point of view) of
\nlong term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows
\nto deal with dimensionality reduction in vectors of time series, in such a way that extracts
\ncommon and specific components. Furthermore, common factors are able to capture not only
\nregular dynamics (stationary or not) but also seasonal one, by means of common factors
\nfollowing a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model.
\nBesides, a bootstrap procedure is proposed to be able to make inference on all the parameters
\ninvolved in the model. A bootstrap scheme developed for forecasting includes uncertainty due
\nto parameter estimation, allowing to enhance the coverage of forecast confidence intervals.
\nConcerning the innovative and challenging application provided, bootstrap procedure developed
\nallows to calculate not only point forecasts but also forecasting intervals for electricity prices.