WARp_acvfs {WRI} | R Documentation |
Function for calculating sample Wasserstein autocovariance functions
Description
This function uses a time series of quantile functions to calculate the sample Wasserstein autocovariance functions from order 0
to p
with a specified training window
Usage
WARp_acvfs(end.day, training.size, quantile, quantile.grid, p)
Arguments
end.day |
A positive integer, the last index of the training window. |
training.size |
A positive integer, the size of the training widnows. |
quantile |
A matrix containing all the available quantile functions. Columns represent time indices and rows represent evaluation grid. |
quantile.grid |
A numeric vector, the grid over which quantile functions are evaluated. |
p |
A positive integer, the maximum order of the sample Wasserstein autocovariance functions. |
Value
A list with
acvfs - The sample Wasserstein autocovariance functions from order
0
top
barycenter - The sample average of the quantile functions in the training window
quantile.pred - The quantile functions from
end.day - p + 1
toend.day