model_conditional_variance

Estimates how the residual variance depends on state variables, used
for constructing the diffusion term of an SDE.

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

model_conditional_variance function

<dl><dt>residuals</dt>
<dd>Numeric vector of residuals</dd>
<dt>predictors</dt>
<dd>Formula or data frame of predictor variables (or vector of names)</dd>
<dt>data</dt>
<dd>Data frame (if predictors is a formula or vector of names)</dd>
<dt>method</dt>
<dd>Modeling method: "symbolic", "linear", "quadratic", "gam", or "constant"</dd>
<dt>transform</dt>
<dd>Transformation of residuals: "squared", "absolute", or "log_squared"</dd>
<dt>...</dt>
<dd>Additional arguments passed to the modeling function</dd></dl>

Arguments

Model Conditional Variance — model_conditional_variance

<dl>

<dt>residuals</dt>
<dd>Numeric vector of residuals</dd>


<dt>predictors</dt>
<dd>Formula or data frame of predictor variables (or vector of names)</dd>


<dt>data</dt>
<dd>Data frame (if predictors is a formula or vector of names)</dd>


<dt>method</dt>
<dd>Modeling method: "symbolic", "linear", "quadratic", "gam", or "constant"</dd>


<dt>transform</dt>
<dd>Transformation of residuals: "squared", "absolute", or "log_squared"</dd>


<dt>...</dt>
<dd>Additional arguments passed to the modeling function</dd>

</dl>

model_conditional_variance: Model Conditional Variance

Description

Usage

Value

Arguments