Identifies outliers in univariate time series using the algorithm lookout.

This is the time series implementation of lookout which identifies outliers in the double differenced time series.

Usage

lookout_ts(x, scale = FALSE, ...)

Arguments

x

The input univariate time series.

scale

If TRUE, the data is standardized. Using the old version, unit scaling is applied so that each column is in the range [0,1]. Under the new version, robust rotation and scaling is used so that the columns are approximately uncorrelated with unit variance. Default is TRUE.

...

Other arguments are passed to lookout.

Value

A lookout object.

See also

lookout

Examples

set.seed(1)
x <- arima.sim(list(order = c(1, 1, 0), ar = 0.8), n = 200)
x[50] <- x[50] + 10
plot(x)

lo <- lookout_ts(x)
lo
#> Leave-out-out KDE outliers using lookout algorithm
#> 
#> Call: lookout(X = u, scale = scale)
#> 
#>    Outliers Probability
#> 1        27           0
#> 2        49           0
#> 5        57           0
#> 6        65           0
#> 7       114           0
#> 8       119           0
#> 9       132           0
#> 10      140           0
#> 11      153           0
#> 12      177           0
#> 14      193           0
#>