Kernel-based prediction of non-Markovian time series

DOI10.1016/j.physd.2020.132829zbMath1490.62289arXiv2007.04286OpenAlexW3041887065MaRDI QIDQ2077859

John Harlim, Faheem Gilani, Dimitrios Giannakis

Publication date: 22 February 2022

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

Full work available at URL: https://arxiv.org/abs/2007.04286

zbMATH Keywords

Nyström methodMori-Zwanzig formalismdelay-embeddingkernel analog forecastMarkovian kernel smoothingnonparametric smoother

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Density estimation (62G07) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (7)

A framework for machine learning of model error in dynamical systems ⋮ Regression-Based Projection for Learning Mori–Zwanzig Operators ⋮ A data-driven statistical-stochastic surrogate modeling strategy for complex nonlinear non-stationary dynamics ⋮ Learning to Forecast Dynamical Systems from Streaming Data ⋮ Ensemble forecasts in reproducing kernel Hilbert space family ⋮ Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions ⋮ Learning stochastic dynamics with statistics-informed neural network

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