SummaryOutliers: Summary Outliers

In SLBDD: Statistical Learning for Big Dependent Data

Summary Outliers

Description

Use the command "tso" of the R package "tsoutliers" to identify outliers for each individual time series.

Usage

SummaryOutliers(
  x,
  type = c("LS", "AO", "TC"),
  tsmethod = "arima",
  args.tsmethod = list(order = c(5, 0, 0))
)

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.
type	A character vector indicating the type of outlier to be considered by the detection procedure. See 'types' in tso function.
tsmethod	The framework for time series modeling. Default is "arima". See 'tsmethod' in tso function.
args.tsmethod	An optional list containing arguments to be passed to the function invoking the method selected in tsmethod. See 'args.tsmethod' in tso function. Default value is c(5,0,0).

Value

A list containing:

• Otable - Summary of various types of outliers detected.

• x.cleaned - Outlier-adjusted data.

• xadja - T-dimensional vector containing the number of time series that have outlier at a given time point.

Examples

data(TaiwanAirBox032017)
output <- SummaryOutliers(TaiwanAirBox032017[1:50,1:3])

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