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Gaussian process for nonstationary time series prediction

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  • Brahim-Belhouari, Sofiane
  • Bermak, Amine

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  • 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
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    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. I. Garcia & J. Jimenez, 2011. "Estimating financial risk using piecewise Gaussian processes," Papers 1112.2889, arXiv.org.
    9. 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.
    10. 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.
    11. 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).
    12. 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.

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