Identify and replace outliers and missing values in a time series

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

Usage

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

Arguments

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.

Value

Time series

References

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

See also

na.interp(), tsoutliers(), stats::supsmu()

Author

Rob J Hyndman

Examples

cleangold <- tsclean(gold)