Non-parametric Gaussian process kernel DMD and LS-SVM predictors revisited A unifying approach

• Type: Article in International Conference Proceedings Book
• Date: 2023

Authors

P. Lopes dos Santos (Author) FEUP; Azevedo-Perdicoulis, TP (Author) Other; Salgado, PA (Author) Other

Conference proceedings International

Title: IFAC-PapersOnLine

22nd IFAC World Congress

Yokohama, 9 July 2023 through 14 July 2023

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Authenticus ID: P-00Z-TDP

DOI: 10.1016/j.ifacol.2023.10.1075

Abstract (EN): In this work, the prediction of a time series is formulated as a gaussian process regression, for different levels of noise. The gaussian regressor is translated into lower rank Dynamic Mode Decomposition methods that use kernels (K-DMD) - Kernel regression and Least Squares Support Vector Machines. The presented unified approach delivers an algorithm where the optimisation of the marginal likelihood function can be used to find the parameters of the kernel regression. The viability of the procedure is demonstrated on a chaotic series, with quite good adjustment results being obtained. Copyright (c) 2023 The Authors.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7