Prediction of high-dimensional time series with exogenous variables using generalized Koopman operator framework in reproducing kernel Hilbert space

DOI10.1007/978-3-319-96944-2_5zbMath1414.62366OpenAlexW2894962288MaRDI QIDQ2419613

Philip H. W. Leong, Farzad Noorian, Gemunu H. Gunaratne, Jia-Chen Hua, Jorge M. Gonçalves

Publication date: 14 June 2019

Full work available at URL: https://doi.org/10.1007/978-3-319-96944-2_5

Gaussian processes Perron-Frobenius operator dynamical system data mining machine learning reproducing kernel Hilbert space high-dimensional time series collective behavior complex system econophysics Koopman operator spatiotemporal dynamics energy forecasting financial markets modeling

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Gaussian processes (60G15) Linear operators in reproducing-kernel Hilbert spaces (including de Branges, de Branges-Rovnyak, and other structured spaces) (47B32)

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