Uses supsmu for non-seasonal series and a robust STL decomposition for seasonal series. To estimate missing values and outlier replacements, linear interpolation is used on the (possibly seasonally adjusted) series

`tsclean(x, replace.missing = TRUE, iterate = 2, lambda = NULL)`

- x
time series

- replace.missing
If TRUE, it not only replaces outliers, but also interpolates missing values

- 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.

Time series

Hyndman (2021) "Detecting time series outliers" https://robjhyndman.com/hyndsight/tsoutliers/.

```
cleangold <- tsclean(gold)
```