A simple pure implementation of Empirical Dynamic Modeling methods in Python. Two methods have been implemented:
- Simplex projection based on "Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series" by Sugihara and May, 1990.
- SMAP based on "Nonlinear forecasting for the classification of natural time series" by Sugihara, 1994.
⚠️ This package is not for production/research. It was created for educational purposes. If you'd like to use an EMD tool for research or production, you should use the pyEDM.
In the example file accompaning the source code (empyred.py) four different examples of timeseries can be found.
- A sinusoidal signal
- Tent Map (default)
- Lorenz system (chaotic regime)
- 3 Species system
- Numpy
- Scipy
- Sklearn
- Matplotlib