causal-ccm 0.3.1

implementation of convergent cross mapping by Sugihara et al (2012)

# 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()`