Python bindings for kEDM

ccm(lib, target, *[, lib_sizes, sample, E, ...])

Estimate the strength of causal interaction between two time series using Convergent Cross Mapping (CCM).

edim(timeseries[, E_max, tau, Tp])

Estimate the optimal embedding dimension of a time series.

simplex(lib, pred, *[, target, E, tau, Tp])

Predict a time series from another using Simplex projection.

eval_simplex(lib, pred, *[, target, E, tau, Tp])

Predict a time series from another using Simplex projection and quantify its predictive skill.

smap(lib, pred, *[, target, E, tau, Tp, theta])

Predict a time series from another using S-Map.

eval_smap(lib, pred, *[, target, E, tau, ...])

Predict a time series from another using S-Map and quantify its predictive skill.

xmap(dataset, edims[, tau, Tp])

Estimate the strength of causal interaction between multiple time series.

get_kokkos_config()

Returns the configuration of Kokkos that kEDM was built with.

Warning

Due to a bug in libgomp (GCC’s OpenMP runtime), kEDM hangs in a forked process. This includes calling kEDM from a multiprocessing.Pool started in fork mode (the default). A workaround is to use spawn mode instead.