Learning dynamical systems from data: a simple cross-validation perspective. III: Irregularly-sampled time series

DOI10.1016/j.physd.2022.133546OpenAlexW3217721411MaRDI QIDQ2677775

Edward De Brouwer, Jonghyeon Lee, Boumediene Hamzi, Houman Owhadi

Publication date: 6 January 2023

Published in: Physica D (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.13037

zbMATH Keywords

kernel flowslearning dynamical systemsirregularly sampled time series

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Time series analysis of dynamical systems (37M10)

Related Items

Learning dynamical systems from data: a simple cross-validation perspective. IV: Case with partial observations ⋮ Gaussian process hydrodynamics ⋮ Ensemble forecasts in reproducing kernel Hilbert space family ⋮ A note on microlocal kernel design for some slow-fast stochastic differential equations with critical transitions and application to EEG signals

Uses Software

• GRU-ODE-Bayes

Cites Work

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