NVAR models exhibit training error scaling laws tied to feature library representation of Lie-series coefficients, with delays reducing one-step error but aiding long-horizon forecasts only under sufficient nonlinearity.
Mezic, Koopman Operator, Geometry, and Learning (2020), arXiv:2010.05377 [math]
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Flow map learning in nonlinear vector autoregressive models: influence of the feature-library structure on the training error
NVAR models exhibit training error scaling laws tied to feature library representation of Lie-series coefficients, with delays reducing one-step error but aiding long-horizon forecasts only under sufficient nonlinearity.