i.qrank: Rank Individual Time Series According to a Given Timewise...

In SLBDD: Statistical Learning for Big Dependent Data

Rank Individual Time Series According to a Given Timewise Quantile Series

Description

Use sum of absolute deviations to select the individual time series that is closest to a given timewise quantile series.

Usage

i.qrank(x, prob = 0.5)

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.
prob	Probability, the quantile series of which is to be computed. Default value is 0.5.

Value

A list containing:

• standardized - A matrix containing standardized time series.

• qts - The timewise quantile of order prob.

• ranks - Rank of the individual time series according to a the given timewise quantile series.

• crit - Sum of absolute deviations of each individual series. Distance of each series to the quantile.

Examples

data(TaiwanAirBox032017)
output <-  i.qrank(TaiwanAirBox032017[,1:3])

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