firstCausalityPointCHECK {patterncausality} | R Documentation |
Checks if the time series data length is sufficient to perform causality analysis based on the provided embedding dimension, time delay, and prediction horizon. This function returns a Boolean indicating the feasibility of conducting the analysis.
firstCausalityPointCHECK(E, tau, h, X)
E |
An integer representing the embedding dimension, which influences the number of dimensions in which the time series is reconstructed for analysis. |
tau |
An integer representing the time delay, used in reconstructing the time series in the embedded space. Note that in this version of the function, 'tau' is not actively used in calculations. |
h |
An integer representing the prediction horizon, indicating how far ahead in the time series the predictions are aimed. |
X |
A numeric vector representing the time series data. |
A boolean value; 'TRUE' if the time series is long enough to accommodate the specified parameters without running out of data, 'FALSE' otherwise.
time_series <- rnorm(1000) # Generate a random time series of 1000 points
embedding_dim <- 3 # Set embedding dimension
time_delay <- 2 # Set time delay (not used in current implementation)
pred_horizon <- 1 # Set prediction horizon
# Check if the first causality point can be considered
is_feasible <- firstCausalityPointCHECK(embedding_dim, time_delay, pred_horizon, time_series)
print(is_feasible)