IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v47y2004i4p705-712.html
   My bibliography  Save this article

Gaussian process for nonstationary time series prediction

Author

Listed:
  • Brahim-Belhouari, Sofiane
  • Bermak, Amine

Abstract

No abstract is available for this item.

Suggested Citation

  • Brahim-Belhouari, Sofiane & Bermak, Amine, 2004. "Gaussian process for nonstationary time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 47(4), pages 705-712, November.
    • Handle: RePEc:eee:csdana:v:47:y:2004:i:4:p:705-712
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00030-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

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

    Citations

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


    Cited by:

    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. Sameh Asim Ajlouni & Moh'd Taleb Alodat, 2021. "Gaussian Process Regression for Forecasting Gasoline Prices in Jordan," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 502-509.
    3. Taichun Qin & Shengkui Zeng & Jianbin Guo & Zakwan Skaf, 2016. "A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena," Energies, MDPI, vol. 9(11), pages 1-18, November.
    4. Cholamjiak, Watcharaporn & Suparatulatorn, Raweerote, 2023. "Strong convergence of a modified extragradient algorithm to solve pseudomonotone equilibrium and application to classification of diabetes mellitus," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    5. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    6. Tao Li & Jinwen Ma, 2023. "Hidden Markov Mixture of Gaussian Process Functional Regression: Utilizing Multi-Scale Structure for Time Series Forecasting," Mathematics, MDPI, vol. 11(5), pages 1-24, March.
    7. Andrawis, Robert R. & Atiya, Amir F. & El-Shishiny, Hisham, 2011. "Forecast combinations of computational intelligence and linear models for the NN5 time series forecasting competition," International Journal of Forecasting, Elsevier, vol. 27(3), pages 672-688, July.
    8. I. Garcia & J. Jimenez, 2011. "Estimating financial risk using piecewise Gaussian processes," Papers 1112.2889, arXiv.org.
    9. M. Alodat & M. AL-Rawwash, 2014. "The extended skew Gaussian process for regression," METRON, Springer;Sapienza Università di Roma, vol. 72(3), pages 317-330, October.
    10. Kieran Wood & Stephen Roberts & Stefan Zohren, 2021. "Slow Momentum with Fast Reversion: A Trading Strategy Using Deep Learning and Changepoint Detection," Papers 2105.13727, arXiv.org, revised Dec 2021.
    11. Oscar Claveria & Enric Monte & Salvador Torra, 2017. "“Regional tourism demand forecasting with machine learning models: Gaussian process regression vs. neural network models in a multiple-input multiple-output setting"," IREA Working Papers 201701, University of Barcelona, Research Institute of Applied Economics, revised Jan 2017.
    12. Yurong Xie & Di Wu & Zhe Qiang, 2023. "An Improved Mixture Model of Gaussian Processes and Its Classification Expectation–Maximization Algorithm," Mathematics, MDPI, vol. 11(10), pages 1-19, May.

    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:eee:csdana:v:47:y:2004:i:4:p:705-712. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.