causal-ccm 0.4.0

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)

Example usage

For an example how to use, see: https://github.com/PrinceJavier/causal_ccm/blob/main/usage_sample.ipynb
Source code: https://github.com/PrinceJavier/causal_ccm

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 import the package
from causal_ccm.causal_ccm import ccm

We define ccm:
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))
ccm1.causality()

We can visualize cross mapping between manifolds of X and Y
ccm1.visualize_cross_mapping()

We visualize correlation of X->Y
We stronger correlation = stronger causal relationship
ccm1.plot_ccm_correls()

Finally, we can check convergence in predictions (correlations) by computing ccm1.causality() for ccm1 defined with different L values.

Additional Feature (PAI)

The pai class implements the Pairwise Asymmetric Inference (see McCracken (2014)). The major difference of pai to ccm is the shadow manifold used to predict X. To create the manifold, use the manifold_pattern and tau parameters. For example, manifold_pattern=[[0, -1, -2],[0]], tau=2 is the same as the shadow manifold (X_t, X_{t-1*2}, X_{t-2*2}, Y_t).

If this package helped you in your work, pls. cite:

@software{Javier_causal-ccm_a_Python_2021,
author = {Javier, Prince Joseph Erneszer},
month = {6},
title = {{causal-ccm a Python implementation of Convergent Cross Mapping}},
version = {0.3.3},
year = {2021}
}