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Forecasting electricity spot price for Nord Pool market with a hybrid k‐factor GARMA–LLWNN model

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  • Souhir Ben Amor
  • Heni Boubaker
  • Lotfi Belkacem

Abstract

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.

Suggested Citation

  • Souhir Ben Amor & Heni Boubaker & Lotfi Belkacem, 2018. "Forecasting electricity spot price for Nord Pool market with a hybrid k‐factor GARMA–LLWNN model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 832-851, December.
    • Handle: RePEc:wly:jforec:v:37:y:2018:i:8:p:832-851
    DOI: 10.1002/for.2544
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    Cited by:

    1. Dorel Mihai Paraschiv & Narciz Balasoiu & Souhir Ben-Amor & Raul Cristian Bag, 2023. "Hybridising Neurofuzzy Model the Seasonal Autoregressive Models for Electricity Price Forecasting on Germany’s Spot Market," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 25(63), pages 463-463, April.
    2. Mtiraoui, Amine & Boubaker, Heni & BelKacem, Lotfi, 2023. "A hybrid approach for forecasting bitcoin series," Research in International Business and Finance, Elsevier, vol. 66(C).
    3. Heni Boubaker & Nawres Bannour, 2023. "Coupling the Empirical Wavelet and the Neural Network Methods in Order to Forecast Electricity Price," JRFM, MDPI, vol. 16(4), pages 1-22, April.
    4. Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila, 2021. "A new BISARMA time series model for forecasting mortality using weather and particulate matter data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(2), pages 346-364, March.
    5. Shobande Olatunji Abdul & Shodipe Oladimeji Tomiwa, 2020. "Re-Evaluation of World Population Figures: Politics and Forecasting Mechanics," Economics and Business, Sciendo, vol. 34(1), pages 104-125, February.

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