Fitting Differential Equations to Time Series Data
Project description
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)
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