forecast (version 8.4)

tsclean: Identify and replace outliers and missing values in a time series

Description

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, lambda = NULL)

Arguments

x

time series

replace.missing

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

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

See Also

na.interp, tsoutliers, supsmu

Examples

Run this code
# NOT RUN {
cleangold <- tsclean(gold)

# }

Run the code above in your browser using DataCamp Workspace