Premium
Forecasting electricity spot price for Nord Pool market with a hybrid k ‐factor GARMA–LLWNN model
Author(s) -
Ben Amor Souhir,
Boubaker Heni,
Belkacem Lotfi
Publication year - 2018
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2544
Subject(s) - electricity price forecasting , econometrics , autoregressive fractionally integrated moving average , series (stratigraphy) , spot contract , wavelet , electricity market , computer science , artificial neural network , electricity , mathematics , algorithm , economics , artificial intelligence , long memory , finance , engineering , volatility (finance) , paleontology , electrical engineering , biology , futures contract
This paper proposes a new hybrid approach, based on the combination of parametric and nonparametric models by adopting wavelet estimation approach, to model and predict the price electricity for Nord Pool market. Our hybrid methodology consists into two steps. The first step aims at modeling the conditional mean of the time series, using a generalized fractional model with k ‐factor of Gegenbauer termed the k ‐factor GARMA model; the parameters of this model are estimated using the wavelet approach based on the discrete wavelet packet transform (DWPT). The second step aims at estimating the conditional variance, so we adopt the local linear wavelet neural network (LLWNN) model. The proposed hybrid model is tested using the hourly log‐returns of electricity spot price from the Nord Pool market. The empirical results were compared with the predictions of the ARFIMA–LLWNN, the k ‐factor GARMA–FIGARCH and the individual LLWNN models. It is shown that the proposed hybrid k ‐factor GARMA–LLWNN model outperforms all other competing methods. Hence it is a robust tool in forecasting time series.