refit.model_mean

Applies a fitted average method model to a new dataset.

Provides a collection of commonly used univariate and multivariate
time series forecasting models including automatically selected exponential
smoothing (ETS) and autoregressive integrated moving average (ARIMA) models.
These models work within the 'fable' framework provided by the 'fabletools'
package, which provides the tools to evaluate, visualise, and combine models
in a workflow consistent with the tidyverse.

Mitchell O'Hara-Wild

fable

Forecasting Models for Tidy Time Series

Rob Hyndman

Earo Wang

Gabriel Caceres

Christoph Bergmeir

Tim-Gunnar Hensel

Timothy Hyndman

refit.model_mean function

<dl><dt>object</dt>
<dd>A model for which forecasts are required.</dd>
<dt>new_data</dt>
<dd>A tsibble containing the time points and exogenous regressors to produce forecasts for.</dd>
<dt>specials</dt>
<dd>(passed by <code>fabletools::forecast.mdl_df()</code>).</dd>
<dt>reestimate</dt>
<dd>If <code>TRUE</code>, the mean for the fitted model will be re-estimated
to suit the new data.</dd>
<dt>...</dt>
<dd>Other arguments passed to methods</dd></dl>

Arguments

Refit a MEAN model — refit.model_mean

<dl>

<dt>object</dt>
<dd>A model for which forecasts are required.</dd>


<dt>new_data</dt>
<dd>A tsibble containing the time points and exogenous regressors to produce forecasts for.</dd>


<dt>specials</dt>
<dd>(passed by <code>fabletools::forecast.mdl_df()</code>).</dd>


<dt>reestimate</dt>
<dd>If <code>TRUE</code>, the mean for the fitted model will be re-estimated
to suit the new data.</dd>


<dt>...</dt>
<dd>Other arguments passed to methods</dd>

</dl>

refit.model_mean: Refit a MEAN model

Description

Usage

Arguments

Examples