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Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market

Author

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  • Ayşe Özmen

    (METU
    University of Calgary)

  • Yuriy Zinchenko

    (University of Calgary)

  • Gerhard-Wilhelm Weber

    (METU
    Poznan University of Technology)

Abstract

Currently, the presence of data uncertainty and noise raises critical issues to be handled on both theoretical and computational grounds. Therefore, robustification and robust optimization have gained attention from theoretical and practical points of view for the establishment of a modeling framework in mathematical optimization to immunize solutions against diverse uncertainties. Data of both the input and output variables, underlying the problems to be addressed, are affected by the noise of different kinds, such that standard statistical models alone may not be sufficient to ensure trustworthy results due to the complexity of the model. Consequently, we propose including parametric uncertainties reflecting future scenarios in multivariate adaptive regression splines (MARS), which, in turn, show great promise for fitting nonlinear multivariate functions, where additive and interactive effects of the predictors are employed to assess the response variable. We robustify MARS through the usage of robust optimization techniques. Due to the large complexity of the underlying model in prior studies, we applied a so-called weak robustification. Now, we exploit a geometrical and combinatorial approach to allow for a more complete robustification, by formulating Robust MARS (RMARS) under Cross-Polytope Uncertainty. In this study, we use RMARS for energy data and demonstrate its superior performance through a simulation study.

Suggested Citation

  • Ayşe Özmen & Yuriy Zinchenko & Gerhard-Wilhelm Weber, 2023. "Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market," Annals of Operations Research, Springer, vol. 324(1), pages 1337-1367, May.
    • Handle: RePEc:spr:annopr:v:324:y:2023:i:1:d:10.1007_s10479-022-04993-w
    DOI: 10.1007/s10479-022-04993-w
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    References listed on IDEAS

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    7. Ayşe Özmen & Gerhard-Wilhelm Weber & Zehra Çavuşoğlu & Özlem Defterli, 2013. "The new robust conic GPLM method with an application to finance: prediction of credit default," Journal of Global Optimization, Springer, vol. 56(2), pages 233-249, June.
    8. Alper Çevik & Gerhard-Wilhelm Weber & B. Murat Eyüboğlu & Kader Karlı Oğuz, 2017. "Voxel-MARS: a method for early detection of Alzheimer’s disease by classification of structural brain MRI," Annals of Operations Research, Springer, vol. 258(1), pages 31-57, November.
    9. Betül Kalaycı & Ayşe Özmen & Gerhard-Wilhelm Weber, 2020. "Mutual relevance of investor sentiment and finance by modeling coupled stochastic systems with MARS," Annals of Operations Research, Springer, vol. 295(1), pages 183-206, December.
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