implementation of convergent cross mapping by Sugihara et al (2012)
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# causal_ccm Package implementing Convergent Cross Mapping for causality inference in dynamical systems as defined by [Sugihara et al (2012)](https://science.sciencemag.org/content/338/6106/496)
See usage_sample.ipynb for an example usage.
## To install pip install causal-ccm
## To use Say we want to check if X drives Y. We first define ccm using: * X and Y - time series data * tau - time lag (if tau=1 we get [t, t-1, t-2…] as our shadow manifold embedding * E - embedding dimension (default=2) for the shadow manifold * L - time horizon to consider, defaults at length of time series X
We define ccm: <br>`ccm1 = ccm(X, Y, tau, E, L) # define ccm with X, Y time series `
We check the strength of causality measured as correlation in prediction vs true (see Sugihara (2012)) <br>`ccm1.causality()`
We can visualize cross mapping between manifolds of X and Y <br>`ccm1.visualize_cross_mapping()`
We visualize correlation of X->Y <br>We stronger correlation = stronger causal relationship <br>`ccm1.plot_ccm_correls()`
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