select_equation

Selects the best equation from the Pareto front using a specified criterion.

A comprehensive toolkit for discovering differential and difference
equations from empirical time series data using symbolic regression. The package
implements a complete workflow from data preprocessing (including Total Variation
Regularized differentiation for noisy economic data), visual exploration of
dynamical structure, and symbolic equation discovery via genetic algorithms.
It leverages a high-performance 'Julia' backend ('SymbolicRegression.jl') to provide
industrial-grade robustness, physics-informed constraints, and rigorous
out-of-sample validation. Designed for economists, physicists, and researchers
studying dynamical systems from observational data.

José Mauricio Gómez Julián

EmpiricalDynamics

Empirical Discovery of Differential Equations from Time Series
Data

select_equation function

<dl><dt>results</dt>
<dd>A symbolic_search_result object.</dd>
<dt>criterion</dt>
<dd>Selection criterion: "knee", "BIC", "AIC", "min_complexity",
or "min_error".</dd>
<dt>n</dt>
<dd>Sample size (required for BIC/AIC if not stored).</dd></dl>

Arguments

Select Equation from Pareto Front — select_equation

<dl>

<dt>results</dt>
<dd>A symbolic_search_result object.</dd>


<dt>criterion</dt>
<dd>Selection criterion: "knee", "BIC", "AIC", "min_complexity",
or "min_error".</dd>


<dt>n</dt>
<dd>Sample size (required for BIC/AIC if not stored).</dd>

</dl>

select_equation: Select Equation from Pareto Front

Description

Usage

Value

Arguments