predict.variance_model

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

predict.variance_model function

<dl><dt>object</dt>
<dd>Variance model object</dd>
<dt>newdata</dt>
<dd>New data for prediction</dd>
<dt>...</dt>
<dd>Additional arguments</dd></dl>

Arguments

Predict from Variance Model — predict.variance_model

<dl>

<dt>object</dt>
<dd>Variance model object</dd>


<dt>newdata</dt>
<dd>New data for prediction</dd>


<dt>...</dt>
<dd>Additional arguments</dd>

</dl>

predict.variance_model: Predict from Variance Model

Description

Usage

Value

Arguments