compute_residuals

Calculates residuals from a fitted symbolic equation or SDE 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

compute_residuals function

<dl><dt>model</dt>
<dd>A symbolic_equation or sde_model object</dd>
<dt>data</dt>
<dd>Data frame containing the variables</dd>
<dt>target</dt>
<dd>Name of target variable (auto-detected if NULL)</dd></dl>

Arguments

Compute Residuals from Symbolic Equation — compute_residuals

<dl>

<dt>model</dt>
<dd>A symbolic_equation or sde_model object</dd>


<dt>data</dt>
<dd>Data frame containing the variables</dd>


<dt>target</dt>
<dd>Name of target variable (auto-detected if NULL)</dd>

</dl>

compute_residuals: Compute Residuals from Symbolic Equation

Description

Usage

Value

Arguments