IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v37y2018i8p832-851.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.2544
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.2544?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jforec:v:37:y:2018:i:8:p:832-851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.