select_lambda_cv_tvr

Selects the regularization parameter lambda using leave-one-out-like
cross-validation.

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_lambda_cv_tvr function

<dl><dt>Z</dt>
<dd>Numeric vector of observations.</dd>
<dt>t</dt>
<dd>Numeric vector of time points.</dd>
<dt>A</dt>
<dd>Integration matrix.</dd>
<dt>D</dt>
<dd>Difference matrix.</dd>
<dt>solver</dt>
<dd>Optimization backend.</dd>
<dt>lambda_seq</dt>
<dd>Sequence of lambda values to evaluate.</dd>
<dt>verbose</dt>
<dd>Print progress?</dd></dl>

Arguments

Cross-Validation Selection of Lambda for TVR — select_lambda_cv_tvr

<dl>

<dt>Z</dt>
<dd>Numeric vector of observations.</dd>


<dt>t</dt>
<dd>Numeric vector of time points.</dd>


<dt>A</dt>
<dd>Integration matrix.</dd>


<dt>D</dt>
<dd>Difference matrix.</dd>


<dt>solver</dt>
<dd>Optimization backend.</dd>


<dt>lambda_seq</dt>
<dd>Sequence of lambda values to evaluate.</dd>


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

</dl>

select_lambda_cv_tvr: Cross-Validation Selection of Lambda for TVR

Description

Usage

Value

Arguments