Identifies outliers in univariate time series using the algorithm lookout. — lookout_ts • 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 default), the data are scaled before the bandwidth and density estimates are computed: with mvscale() when old_version = FALSE, so that the columns are approximately uncorrelated with unit scale, or by scaling each column to the range [0, 1] when old_version = TRUE.
...: Other arguments are passed to lookout.

Value

A lookout object.

See also

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
#> 2       50 0.000000000
#> 4      177 0.006841116
#>