Year: 2016
Type: article
Abstract: We compare two approaches to the predictive modeling of dynamical systems from partial observations at discrete times. The first is continuous in time, where one uses data to infer a model in the form of stochastic differential equations, which are then discretized for numerical solution. The second is discrete in time, where one directly infers a discrete-time model in the form of a nonlinear autoregression moving average model. The comparison is performed in a special case where the observations are known to have been obtained from a hypoelliptic stochastic differential equation. We show that the discrete-time approach has better predictive skills, especially when the data are relatively sparse in time. We discuss open questions as well as the broader significance of the results.
Authors: Fei Lu, Kevin Lin, Alexandre Chorin
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G1286236842, G1863003419, Computational Nonlinear Dynamics: Variance Reduction Methods and Numerical Studies of Large, Chaotic, and Noisy Systems, G3083819904, G419881185, G42916606, G4501827968, G498139845, G6652958823, Data Assimilation, Noise Models, and Dimensional Reduction, with Applications, G969889393
OpenAlex ID: W2364569407