outlierLasso: Outliers LASSO

In SLBDD: Statistical Learning for Big Dependent Data

Outliers LASSO

Description

Use LASSO estimation to identify outliers in a set of time series by creating dummy variables for every time point.

Usage

outlierLasso(
  zt,
  p = 12,
  crit = 3.5,
  family = "gaussian",
  standardize = TRUE,
  alpha = 1,
  jend = 3
)

Arguments

zt	T by 1 vector of an observed scalar time series without missing values.
p	Seasonal period. Default value is 12.
crit	Criterion. Default is 3.5.
family	Response type. See the glmnet command in R. Possible types are "gaussian", "binomial", "poisson", "multinomial", "cox", "mgaussian". Default is "gaussian".
standardize	Logical flag for zt variable standardization. See the glmnet command in R. Default is TRUE.
alpha	Elasticnet mixing parameter, with 0 ≤q α ≤q 1. See the glmnet command in R. Default value is 1.
jend	Number of first and last observations assumed to not be level shift outliers. Default value is 3.

Value

A list containing:

• nAO - Number of additive outliers.

• nLS - Number of level shifts.

Examples

data(TaiwanAirBox032017)
output <- outlierLasso(TaiwanAirBox032017[1:100,1])

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