R: Identify and replace outliers and missing values in a time...

tsclean {forecast} | R Documentation

## 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

### Author(s)

Rob J Hyndman

### See Also

`na.interp`, `tsoutliers`, `supsmu`

### Examples

    cleangold <- tsclean(gold)

[Package forecast version 8.12 Index]