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 using`BoxCox.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
#>
```