Empirical Dynamic Modeling (EDM)
This package provides a Python/Pandas DataFrame toolset for EDM analysis. Introduction and documentation are are avilable online, or in the package API docs. A Jupyter notebook interface is available at jpyEDM.
Functionality includes:
- Simplex projection (Sugihara and May 1990)
- Sequential Locally Weighted Global Linear Maps (S-Map) (Sugihara 1994)
- Multivariate embeddings (Dixon et. al. 1999)
- Convergent cross mapping (Sugihara et. al. 2012)
- Multiview embedding (Ye and Sugihara 2016)
Installation
Python Package Index (PyPI)
Certain MacOS, Linux and Windows platforms are supported with prebuilt binary distributions hosted on PyPI pyEDM and can be installed with the Python pip module: python -m pip install pyEDM
Usage
Examples can be executed in the python command line:
>>> import pyEDM
>>> pyEDM.Examples()
References
Sugihara G. and May R. 1990. Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series. Nature, 344:734–741.
Sugihara G. 1994. Nonlinear forecasting for the classification of natural time series. Philosophical Transactions: Physical Sciences and Engineering, 348 (1688) : 477–495.
Dixon, P. A., M. Milicich, and G. Sugihara, 1999. Episodic fluctuations in larval supply. Science 283:1528–1530.
Sugihara G., May R., Ye H., Hsieh C., Deyle E., Fogarty M., Munch S., 2012. Detecting Causality in Complex Ecosystems. Science 338:496-500.
Ye H., and G. Sugihara, 2016. Information leverage in interconnected ecosystems: Overcoming the curse of dimensionality. Science 353:922–925.
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