symbolic_search_weighted

Performs symbolic search with weighted least squares.

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

symbolic_search_weighted function

<dl><dt>target</dt>
<dd>Numeric vector of target values (typically derivatives).</dd>
<dt>predictors</dt>
<dd>Data frame of predictor variables.</dd>
<dt>weights</dt>
<dd>Optional weight vector for weighted regression.</dd>
<dt>operators</dt>
<dd>List specifying allowed operators:<ul>
<li>binary: c("+", "-", "*", "/")</li>
<li>unary: c("exp", "log", "sqrt", "inv", "square")</li>
<li>custom: Custom function names (must be defined)</li>
</ul></dd>
<dt>constraints</dt>
<dd>List of constraints:<ul>
<li>forced: Formula of terms that must appear</li>
<li>forbidden: Formula of terms that must not appear</li>
<li>max_complexity: Maximum expression complexity</li>
</ul></dd>
<dt>n_runs</dt>
<dd>Number of independent runs for robustness.</dd>
<dt>complexity_penalty</dt>
<dd>Penalty per unit complexity.</dd>
<dt>verbose</dt>
<dd>Print progress messages?</dd></dl>

Arguments

Weighted Symbolic Search — symbolic_search_weighted

<dl>

<dt>target</dt>
<dd>Numeric vector of target values (typically derivatives).</dd>


<dt>predictors</dt>
<dd>Data frame of predictor variables.</dd>


<dt>weights</dt>
<dd>Optional weight vector for weighted regression.</dd>


<dt>operators</dt>
<dd>List specifying allowed operators:<ul>
<li>binary: c("+", "-", "*", "/")</li>
<li>unary: c("exp", "log", "sqrt", "inv", "square")</li>
<li>custom: Custom function names (must be defined)</li>
</ul></dd>


<dt>constraints</dt>
<dd>List of constraints:<ul>
<li>forced: Formula of terms that must appear</li>
<li>forbidden: Formula of terms that must not appear</li>
<li>max_complexity: Maximum expression complexity</li>
</ul></dd>


<dt>n_runs</dt>
<dd>Number of independent runs for robustness.</dd>


<dt>complexity_penalty</dt>
<dd>Penalty per unit complexity.</dd>


<dt>verbose</dt>
<dd>Print progress messages?</dd>

</dl>

symbolic_search_weighted: Weighted Symbolic Search

Description

Usage

Value

Arguments