deFit 0.1.2

Fitting Differential Equations to Time Series Data

Welcome to deFit

Fitting Differential Equations to Time Series Data ( deFit ).

Overview

What is deFit?

Use numerical optimization to fit ordinary differential equations (ODEs) to time series data to examine the dynamic relationships between variables or the characteristics of a dynamical system. It can now be used to estimate the parameters of ODEs up to second order.

Features

• Fit ordinary differential equation models to time series data
• Report model parameter estimations, standard errors, R-squared, and root mean standard error
• Plot raw data points and fitted lines
• Support ordinary differential equation models up to second order
• deFit can run in Python and R environments

1.2 First impression in Python

To get a first impression of how deFit works in simulation, consider the following example of a differential equational model. The figure below contains a graphical representation of the model that we want to fit.

import defit
import pandas as pd
df1 = pd.read_csv('defit/data/example1.csv')
model1 = '''
            x =~ myX
            time =~ myTime
            x(2) ~ x + x(1)
        '''
result1 = defit.defit(data=df1,model=model1)

2 Navigation

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• User guide in R
• User guide in Python
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