edqts: Empirical Dynamic Quantile for Visualization of...

In SLBDD: Statistical Learning for Big Dependent Data

Empirical Dynamic Quantile for Visualization of High-Dimensional Time Series

Description

Compute empirical dynamic quantile (EDQ) for a given probability "p" based on the weighted algorithm proposed in the article by Peña, Tsay and Zamar (2019).

Usage

edqts(x, p = 0.5, h = 30)

Arguments

x	T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.
p	Probability, the quantile series of which is to be computed. Default value is 0.5.
h	Number of time series observations used in the algorithm. The larger h is the longer to compute. Default value is 30.

Value

The column of the matrix x which stores the "p" EDQ of interest.

References

Peña, D. Tsay, R. and Zamar, R. (2019). Empirical Dynamic Quantiles for Visualization of High-Dimensional Time Series, Technometrics, 61:4, 429-444.

Examples

data(TaiwanAirBox032017)
edqts(TaiwanAirBox032017[,1:25])

SLBDD documentation built on April 27, 2022, 5:08 p.m.