IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v33y2006i9p925-941.html
   My bibliography  Save this article

Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers

Author

Listed:
  • Mak Kaboudan

Abstract

Monthly average sunspot numbers follow irregular cycles with complex nonlinear dynamics. Statistical linear models constructed to forecast them are therefore inappropriate, while nonlinear models produce solutions sensitive to initial conditions. Two computational techniques - neural networks and genetic programming - that have their advantages are applied instead to the monthly numbers and their wavelet-transformed and wavelet-denoised series. The objective is to determine if modeling wavelet-conversions produces better forecasts than those from modeling series' observed values. Because sunspot numbers are indicators of geomagnetic activity their forecast is important. Geomagnetic storms endanger satellites and disrupt communications and power systems on Earth.

Suggested Citation

  • Mak Kaboudan, 2006. "Computational Forecasting of Wavelet-converted Monthly Sunspot Numbers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(9), pages 925-941.
    • Handle: RePEc:taf:japsta:v:33:y:2006:i:9:p:925-941
    DOI: 10.1080/02664760600744215
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/02664760600744215
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664760600744215?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Darbellay, Georges A. & Slama, Marek, 2000. "Forecasting the short-term demand for electricity: Do neural networks stand a better chance?," International Journal of Forecasting, Elsevier, vol. 16(1), pages 71-83.
    2. F. Abramovich & T. Sapatinas & B. W. Silverman, 1998. "Wavelet thresholding via a Bayesian approach," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(4), pages 725-749.
    3. M. M. Gabr & T. Subba Rao, 1981. "The Estimation And Prediction Of Subset Bilinear Time Series Models With Applications," Journal of Time Series Analysis, Wiley Blackwell, vol. 2(3), pages 155-171, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marco Di Zio & Arnoldo Frigessi, 1999. "Smoothness in Bayesian Non-parametric Regression with Wavelets," Methodology and Computing in Applied Probability, Springer, vol. 1(4), pages 391-405, December.
    2. Hu, Junjie & López Cabrera, Brenda & Melzer, Awdesch, 2021. "Advanced statistical learning on short term load process forecasting," IRTG 1792 Discussion Papers 2021-020, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    3. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    4. Nikos Sakkas & Sofia Yfanti & Pooja Shah & Nikitas Sakkas & Christina Chaniotakis & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2023. "Explainable Approaches for Forecasting Building Electricity Consumption," Energies, MDPI, vol. 16(20), pages 1-20, October.
    5. Rocha Souza, Leonardo & Jorge Soares, Lacir, 2007. "Electricity rationing and public response," Energy Economics, Elsevier, vol. 29(2), pages 296-311, March.
    6. Francesco Battaglia & Lia Orfei, 2005. "Outlier Detection And Estimation In NonLinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 26(1), pages 107-121, January.
    7. A. Antoniadis, 1997. "Rejoinder," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 6(2), pages 143-144, August.
    8. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    9. Wei-Chiang Hong & Yucheng Dong & Chien-Yuan Lai & Li-Yueh Chen & Shih-Yung Wei, 2011. "SVR with Hybrid Chaotic Immune Algorithm for Seasonal Load Demand Forecasting," Energies, MDPI, vol. 4(6), pages 1-18, June.
    10. Matthieu Garcin & Dominique Guegan, 2015. "Optimal wavelet shrinkage of a noisy dynamical system with non-linear noise impact," Documents de travail du Centre d'Economie de la Sorbonne 15085, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    11. William Irungu Nganga & Julien Chevallier & Simon Wagura Ndiritu, 2018. "Regime changes and fiscal sustainability in Kenya with comparative nonlinear Granger causalities across East-African countries," Working Papers halshs-01941226, HAL.
    12. Amaral, Luiz Felipe & Souza, Reinaldo Castro & Stevenson, Maxwell, 2008. "A smooth transition periodic autoregressive (STPAR) model for short-term load forecasting," International Journal of Forecasting, Elsevier, vol. 24(4), pages 603-615.
    13. Ohtsuka, Yoshihiro & Oga, Takashi & Kakamu, Kazuhiko, 2010. "Forecasting electricity demand in Japan: A Bayesian spatial autoregressive ARMA approach," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2721-2735, November.
    14. Soares, Lacir Jorge & Souza, Leonardo Rocha, 2006. "Forecasting electricity demand using generalized long memory," International Journal of Forecasting, Elsevier, vol. 22(1), pages 17-28.
    15. Jaume Rosselló Nadal & Mohcine Bakhat, 2009. "A new approach to estimating tourism-induced electricity consumption," CRE Working Papers (Documents de treball del CRE) 2009/6, Centre de Recerca Econòmica (UIB ·"Sa Nostra").
    16. Yu Yue & Paul Speckman & Dongchu Sun, 2012. "Priors for Bayesian adaptive spline smoothing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(3), pages 577-613, June.
    17. Beran, Jan & Heiler, Mark A., 2008. "A nonparametric regression cross spectrum for multivariate time series," Journal of Multivariate Analysis, Elsevier, vol. 99(4), pages 684-714, April.
    18. Nilotpal Sanyal & Marco A. R. Ferreira, 2017. "Bayesian Wavelet Analysis Using Nonlocal Priors with an Application to fMRI Analysis," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 79(2), pages 361-388, November.
    19. Hee-Seok Oh & Donghoh Kim & Youngjo Lee, 2009. "Cross-validated wavelet shrinkage," Computational Statistics, Springer, vol. 24(3), pages 497-512, August.
    20. repec:qut:auncer:wp103 is not listed on IDEAS
    21. Christopher Bennett & Rodney A. Stewart & Junwei Lu, 2014. "Autoregressive with Exogenous Variables and Neural Network Short-Term Load Forecast Models for Residential Low Voltage Distribution Networks," Energies, MDPI, vol. 7(5), pages 1-23, April.

    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:taf:japsta:v:33:y:2006:i:9:p:925-941. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.