compute_derivative_tvr

Computes derivatives by solving a convex optimization problem that balances
fidelity to the data against smoothness of the derivative.

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_derivative_tvr function

<dl><dt>Z</dt>
<dd>Numeric vector of observations.</dd>
<dt>t</dt>
<dd>Numeric vector of time points (NULL assumes dt=1).</dd>
<dt>lambda</dt>
<dd>Regularization parameter ("auto" for cross-validation selection).</dd>
<dt>solver</dt>
<dd>Optimization backend: "osqp", "ecos", or "scs".</dd>
<dt>...</dt>
<dd>Additional arguments (ignored).</dd></dl>

Arguments

Total Variation Regularized Differentiation — compute_derivative_tvr

<dl>

<dt>Z</dt>
<dd>Numeric vector of observations.</dd>


<dt>t</dt>
<dd>Numeric vector of time points (NULL assumes dt=1).</dd>


<dt>lambda</dt>
<dd>Regularization parameter ("auto" for cross-validation selection).</dd>


<dt>solver</dt>
<dd>Optimization backend: "osqp", "ecos", or "scs".</dd>


<dt>...</dt>
<dd>Additional arguments (ignored).</dd>

</dl>

compute_derivative_tvr: Total Variation Regularized Differentiation

Description

Usage

Value

Arguments