trend

This function is used to specify the preprocessing associated with the trend
of a time series.

An engine for univariate time series forecasting using
different regression models in an autoregressive way. The engine
provides an uniform interface for applying the different models.
Furthermore, it is extensible so that users can easily apply their
own regression models to univariate time series forecasting and
benefit from all the features of the engine, such as preprocessings
or estimation of forecast accuracy.

Francisco Martinez

utsf

Univariate Time Series Forecasting

Maria Pilar Frias-Bustamante

trend function

<dl><dt>type</dt>
<dd>A character indicating the type of preprocessing applied to the
time series. Possible values are: <code>"none</code>, <code>"additive"</code>, <code>"multiplicative"</code>
and <code>"differences"</code>.</dd>
<dt>n</dt>
<dd>An integer specifying the order of first differences to be applied.
If the default (-1) is used, the order of first differences needed by the
time series will be estimated by the <code>forecast::ndiffs()</code> function. This
parameter is only meant when the <code>type</code> parameter is <code>"differences"</code>.</dd></dl>

Arguments

Specifying the transformation for dealing with trended series — trend

<dl>

<dt>type</dt>
<dd>A character indicating the type of preprocessing applied to the
time series. Possible values are: <code>"none</code>, <code>"additive"</code>, <code>"multiplicative"</code>
and <code>"differences"</code>.</dd>


<dt>n</dt>
<dd>An integer specifying the order of first differences to be applied.
If the default (-1) is used, the order of first differences needed by the
time series will be estimated by the <code>forecast::ndiffs()</code> function. This
parameter is only meant when the <code>type</code> parameter is <code>"differences"</code>.</dd>

</dl>

trend: Specifying the transformation for dealing with trended series

Description

Usage

Value

Arguments

Examples