Analyzes pattern causality relationships between multiple time series in X and multiple time series in Y by computing pairwise causality measures and organizing them into a matrix.
Usage
pcCrossMatrix(
X,
Y,
E,
tau,
metric = "euclidean",
h,
weighted = TRUE,
distance_fn = NULL,
state_space_fn = NULL,
verbose = FALSE,
n_cores = 1
)Arguments
- X
Matrix or data frame of time series for the cause
- Y
Matrix or data frame of time series for the effect
- E
Integer; embedding dimension
- tau
Integer; time delay
- metric
Character; distance metric ("euclidean", "manhattan", "maximum")
- h
Integer; prediction horizon
- weighted
Logical; whether to use weighted causality
- distance_fn
Optional custom distance function
- state_space_fn
Optional custom state space reconstruction function
- verbose
Logical; whether to print progress
- n_cores
Integer; number of cores for parallel computation