plot_pareto_front

Visualizes the trade-off between equation complexity and fit quality.

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

plot_pareto_front function

<dl><dt>results</dt>
<dd>A symbolic_search_result object.</dd>
<dt>highlight_selection</dt>
<dd>Which equation to highlight ("knee", "BIC", "AIC", or index).</dd>
<dt>show_all</dt>
<dd>Show all equations or just Pareto front?</dd></dl>

Arguments

Plot Pareto Front — plot_pareto_front

<dl>

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


<dt>highlight_selection</dt>
<dd>Which equation to highlight ("knee", "BIC", "AIC", or index).</dd>


<dt>show_all</dt>
<dd>Show all equations or just Pareto front?</dd>

</dl>

plot_pareto_front: Plot Pareto Front

Description

Usage

Value

Arguments