Parameter Estimation Through Ignorance

TL;DR

This paper introduces a novel, practical method for estimating parameters in nonlinear dynamical systems by optimizing forecast accuracy, outperforming traditional methods like least squares especially with non-Gaussian errors.

Contribution

It presents a new parameter estimation technique based on forecast skill scores, applicable to nonlinear systems, and introduces measures of model inadequacy called 'Implied Ignorance' and 'information deficit.'

Findings

Outperforms linear least squares in nonlinear systems.

More effective with non-Gaussian forecast errors.

Applicable to complex systems like Lorenz96, Hénon, and Logistic Map.

Abstract

Dynamical modelling lies at the heart of our understanding of physical systems. Its role in science is deeper than mere operational forecasting, in that it allows us to evaluate the adequacy of the mathematical structure of our models. Despite the importance of model parameters, there is no general method of parameter estimation outside linear systems. A new relatively simple method of parameter estimation for nonlinear systems is presented, based on variations in the accuracy of probability forecasts. It is illustrated on the Logistic Map, the Henon Map and the 12-D Lorenz96 flow, and its ability to outperform linear least squares in these systems is explored at various noise levels and sampling rates. As expected, it is more effective when the forecast error distributions are non-Gaussian. The new method selects parameter values by minimizing a proper, local skill score for continuous probability forecasts as a function of the parameter values. This new approach is easier to implement in practice than alternative nonlinear methods based on the geometry of attractors or the ability of the model to shadow the observations. New direct measures of inadequacy in the model, the "Implied Ignorance" and the information deficit are introduced.