Gradient boosting with moving-average terms for nonlinear sequential regression

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buir.contributor.authorİlhan, Emirhan
buir.contributor.authorTuralı, Mehmet Yiğit
buir.contributor.authorKozat, Suleyman Serdar
buir.contributor.orcidİlhan, Emirhan|0009-0000-5423-8960
buir.contributor.orcidTuralı, Mehmet Yiğit|0000-0002-6147-1741
buir.contributor.orcidKozat, Suleyman Serdar|0000-0002-6488-3848
dc.citation.epage1186en_US
dc.citation.spage1182
dc.citation.volumeNumber30
dc.contributor.authorİlhan, Emirhan
dc.contributor.authorTuralı, Mehmet Yiğit
dc.contributor.authorKozat, Suleyman Serdar.
dc.date.accessioned2024-03-14T10:00:03Z
dc.date.available2024-03-14T10:00:03Z
dc.date.issued2023-08-23
dc.departmentDepartment of Electrical and Electronics Engineering
dc.description.abstractWe investigate sequential nonlinear regression and introduce a novel gradient boosting algorithm that exploits the residuals, i.e., prediction errors, as additional features, as inspired by the well-known linear auto-regressive-moving-average (ARMA) models. Our algorithm utilizes the state information from early time steps contained in the residuals to improve the performance in a nonlinear sequential regression/prediction framework. The algorithm exploits the changes in the previous time steps through residual terms between boosting steps by jointly optimizing the model parameters and the feature vectors. For this optimization, we define an iterative procedure in which we alternate between two steps where the former evaluates the optimal base learner parameters for fixed residual values, and the latter updates the residuals given the new parameters. We show through both artificial and well-known real-life competition datasets that our method significantly outperforms the state-of-the-art.
dc.identifier.doi10.1109/LSP.2023.3309577
dc.identifier.eissn1558-2361
dc.identifier.issn1070-9908
dc.identifier.urihttps://hdl.handle.net/11693/114734
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.relation.isversionofhttps://dx.doi.org/10.1109/LSP.2023.3309577
dc.rightsCC BY
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.source.titleIEEE Signal Processing Letters
dc.subjectARMA
dc.subjectGradient boosting machine (GBM)
dc.subjectNonlinear models
dc.subjectSequential learning
dc.titleGradient boosting with moving-average terms for nonlinear sequential regression
dc.typeArticle

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