WARp_acvfs: Function for calculating sample Wasserstein autocovariance...

In WRI: Wasserstein Regression and Inference

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 to p

• barycenter - The sample average of the quantile functions in the training window

• quantile.pred - The quantile functions from end.day - p + 1 to end.day

WRI documentation built on July 9, 2022, 1:06 a.m.