High-dimensional time series prediction using kernel-based koopman mode regression

DOI10.1007/s11071-017-3764-yOpenAlexW2756023359MaRDI QIDQ1696900

Gemunu H. Gunaratne, Philip H. W. Leong, Jia-Chen Hua, Duncan Moss, Farzad Noorian

Publication date: 15 February 2018

Published in: Nonlinear Dynamics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11071-017-3764-y

zbMATH Keywords

spatio-temporal dynamicskernel methodscomplex systemshigh-dimensional time seriesdynamic mode decompositiondata-driven Koopman operator

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning, Forecasting of nonlinear dynamics based on symbolic invariance, Sparsity-promoting algorithms for the discovery of informative Koopman-invariant subspaces, Koopman-based spectral clustering of directed and time-evolving graphs

Uses Software

• forecast
• Forecast
• AdaBoost.MH
• PRMLT

Cites Work

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