read_empirical_data

Reads time series or panel data from various file formats and prepares
it for use with EmpiricalDynamics functions.

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

read_empirical_data function

<dl><dt>file</dt>
<dd>Path to the data file (CSV, RDS, or RData).</dd>
<dt>time_col</dt>
<dd>Name of the time/date column (auto-detected if NULL).</dd>
<dt>date_format</dt>
<dd>Date format string if time column is character.</dd>
<dt>...</dt>
<dd>Additional arguments passed to read.csv.</dd></dl>

Arguments

Read Empirical Data from File — read_empirical_data

<dl>

<dt>file</dt>
<dd>Path to the data file (CSV, RDS, or RData).</dd>


<dt>time_col</dt>
<dd>Name of the time/date column (auto-detected if NULL).</dd>


<dt>date_format</dt>
<dd>Date format string if time column is character.</dd>


<dt>...</dt>
<dd>Additional arguments passed to read.csv.</dd>

</dl>

read_empirical_data: Read Empirical Data from File

Description

Usage

Value

Arguments