bootstrap_parameters

Computes bootstrap confidence intervals for equation parameters.

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

bootstrap_parameters function

<dl><dt>equation</dt>
<dd>Fitted equation object.</dd>
<dt>data</dt>
<dd>Original data.</dd>
<dt>response</dt>
<dd>Name of response column.</dd>
<dt>derivative_col</dt>
<dd>Alias for response.</dd>
<dt>n_boot</dt>
<dd>Number of bootstrap samples.</dd>
<dt>conf_level</dt>
<dd>Confidence level (default 0.95).</dd>
<dt>block_size</dt>
<dd>Block size for block bootstrap (time series).</dd></dl>

Arguments

Bootstrap Confidence Intervals for Parameters — bootstrap_parameters

<dl>

<dt>equation</dt>
<dd>Fitted equation object.</dd>


<dt>data</dt>
<dd>Original data.</dd>


<dt>response</dt>
<dd>Name of response column.</dd>


<dt>derivative_col</dt>
<dd>Alias for response.</dd>


<dt>n_boot</dt>
<dd>Number of bootstrap samples.</dd>


<dt>conf_level</dt>
<dd>Confidence level (default 0.95).</dd>


<dt>block_size</dt>
<dd>Block size for block bootstrap (time series).</dd>

</dl>

bootstrap_parameters: Bootstrap Confidence Intervals for Parameters

Description

Usage

Value

Arguments