estimate_sde_iterative

Refines drift and diffusion estimates using iterative Generalized Least
Squares, which is more appropriate when heteroscedasticity is substantial.

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

estimate_sde_iterative function

<dl><dt>target</dt>
<dd>Numeric vector of target values (derivatives)</dd>
<dt>predictors</dt>
<dd>Data frame of predictor variables</dd>
<dt>data</dt>
<dd>Full data frame</dd>
<dt>initial_drift</dt>
<dd>Initial drift equation (optional)</dd>
<dt>max_iter</dt>
<dd>Maximum number of iterations</dd>
<dt>tol</dt>
<dd>Convergence tolerance (RMSE change in coefficients)</dd></dl>

Arguments

Iterative GLS Estimation for SDEs — estimate_sde_iterative

<dl>

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


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


<dt>data</dt>
<dd>Full data frame</dd>


<dt>initial_drift</dt>
<dd>Initial drift equation (optional)</dd>


<dt>max_iter</dt>
<dd>Maximum number of iterations</dd>


<dt>tol</dt>
<dd>Convergence tolerance (RMSE change in coefficients)</dd>

</dl>

estimate_sde_iterative: Iterative GLS Estimation for SDEs

Description

Usage

Value

Arguments