outlier.plot: Find Outliers Using an Upper and a Lower Timewise Quantile...

In SLBDD: Statistical Learning for Big Dependent Data

Find Outliers Using an Upper and a Lower Timewise Quantile Series

Description

Use an upper and a lower timewise quantile series to highlight the possible outliers in a collection of time series.

Usage

outlier.plot(x, prob = 0.05, percent = 0.05, xtime = NULL)

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	Tail probability. That is, the two quantile series is (prob, 1-prob). prob is restricted to be in (0,0.15). Default value is 0.05.
percent	The number of possible outliers in each side is T*k*prob*percent.
xtime	A vector with the values for the x labels. Default values are 1, 2, 3, ...

Value

A list containing:

• standardized - A matrix containing standardized time series.

• qts - The timewise quantile of order prob.

• minseries - The timewise minimum of the standardized time series.

• maxseries - The timewise maximum of the standardized time series.

Examples

data(TaiwanAirBox032017)
output <-  outlier.plot(TaiwanAirBox032017[,1:3])

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