outlierLasso function

Outliers LASSO

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

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 $0 \leq \alpha \leq 1$. 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.

Returns

A list containing:

• nAO - Number of additive outliers.
• nLS - Number of level shifts.

Examples

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