tsclean

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

Methods and tools for displaying and analysing
univariate time series forecasts including exponential smoothing
via state space models and automatic ARIMA modelling.

Rob Hyndman

forecast

Forecasting Functions for Time Series and Linear Models

George Athanasopoulos

Christoph Bergmeir

Gabriel Caceres

Leanne Chhay

Kirill Kuroptev

Mitchell O'Hara-Wild

Fotios Petropoulos

Slava Razbash

Earo Wang

Farah Yasmeen

Daniel Reid

David Shaub

Federico Garza

R Core Team 

Ross Ihaka

Xiaoqian Wang

Yuan Tang

Zhenyu Zhou

tsclean function

<dl><dt>x</dt>
<dd>time series</dd>
<dt>replace.missing</dt>
<dd>If TRUE, it not only replaces outliers, but also
interpolates missing values</dd>
<dt>iterate</dt>
<dd>the number of iterations required</dd>
<dt>lambda</dt>
<dd>Box-Cox transformation parameter. If <code>lambda="auto"</code>,
then a transformation is automatically selected using <code>BoxCox.lambda</code>.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.</dd></dl>

Arguments

Author

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

<dl>

<dt>x</dt>
<dd>time series</dd>


<dt>replace.missing</dt>
<dd>If TRUE, it not only replaces outliers, but also
interpolates missing values</dd>


<dt>iterate</dt>
<dd>the number of iterations required</dd>


<dt>lambda</dt>
<dd>Box-Cox transformation parameter. If <code>lambda="auto"</code>,
then a transformation is automatically selected using <code>BoxCox.lambda</code>.
The transformation is ignored if NULL. Otherwise,
data transformed before model is estimated.</dd>

</dl>

Identify and replace outliers and missing values in a time series

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

Description

Usage

Value

Arguments

Author

References

See Also

Examples