Uses supsmu for non-seasonal series and a periodic stl decomposition with seasonal series to identify outliers and estimate their replacements.
Arguments
- x
time series
- iterate
the number of iterations required
- lambda
Box-Cox transformation parameter. If
lambda="auto"
, then a transformation is automatically selected usingBoxCox.lambda
. The transformation is ignored if NULL. Otherwise, data transformed before model is estimated.
Value
- index
Indicating the index of outlier(s)
- replacement
Suggested numeric values to replace identified outliers
References
Hyndman (2021) "Detecting time series outliers" https://robjhyndman.com/hyndsight/tsoutliers/.
Examples
data(gold)
tsoutliers(gold)
#> $index
#> [1] 770
#>
#> $replacements
#> [1] 494.9
#>