suggest_differentiation_method

Analyzes the time series to recommend the most appropriate differentiation
method based on detected features like trend, periodicity, shocks, and noise.

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

suggest_differentiation_method function

<dl><dt>Z</dt>
<dd>Numeric vector of observations.</dd>
<dt>t</dt>
<dd>Numeric vector of time points.</dd></dl>

Arguments

Suggest Differentiation Method Based on Data Characteristics — suggest_differentiation_method

<dl>

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


<dt>t</dt>
<dd>Numeric vector of time points.</dd>

</dl>

suggest_differentiation_method: Suggest Differentiation Method Based on Data Characteristics

Description

Usage

Value

Arguments

Examples