plot_timeseries

Creates a time series plot with optional trend line and change point detection.

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

plot_timeseries function

<dl><dt>data</dt>
<dd>Data frame.</dd>
<dt>var</dt>
<dd>Variable name to plot.</dd>
<dt>time</dt>
<dd>Time variable name.</dd>
<dt>show_trend</dt>
<dd>Add trend line?</dd>
<dt>highlight_changes</dt>
<dd>Highlight potential structural breaks?</dd></dl>

Arguments

Time Series Plot — plot_timeseries

<dl>

<dt>data</dt>
<dd>Data frame.</dd>


<dt>var</dt>
<dd>Variable name to plot.</dd>


<dt>time</dt>
<dd>Time variable name.</dd>


<dt>show_trend</dt>
<dd>Add trend line?</dd>


<dt>highlight_changes</dt>
<dd>Highlight potential structural breaks?</dd>

</dl>

plot_timeseries: Time Series Plot

Description

Usage

Value

Arguments