forecast.NNETAR

Produces forecasts from a trained model.

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

forecast.NNETAR 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>simulate</dt>
<dd>If <code>TRUE</code>, forecast distributions are produced by sampling from a normal distribution. Without simulation, forecast uncertainty cannot be estimated for this model and instead a degenerate distribution with the forecast mean will be produced.</dd>
<dt>bootstrap</dt>
<dd>If <code>TRUE</code>, forecast distributions are produced by sampling from the model's training residuals.</dd>
<dt>times</dt>
<dd>The number of sample paths to use in producing the forecast distribution. Setting <code>simulate = FALSE</code> or <code>times = 0</code> will produce degenerate forecast distributions of the forecast mean.</dd>
<dt>...</dt>
<dd>Other arguments passed to methods</dd></dl>

Arguments

Forecast a model from the fable package — forecast.NNETAR

<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>simulate</dt>
<dd>If <code>TRUE</code>, forecast distributions are produced by sampling from a normal distribution. Without simulation, forecast uncertainty cannot be estimated for this model and instead a degenerate distribution with the forecast mean will be produced.</dd>


<dt>bootstrap</dt>
<dd>If <code>TRUE</code>, forecast distributions are produced by sampling from the model's training residuals.</dd>


<dt>times</dt>
<dd>The number of sample paths to use in producing the forecast distribution. Setting <code>simulate = FALSE</code> or <code>times = 0</code> will produce degenerate forecast distributions of the forecast mean.</dd>


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

</dl>

forecast.NNETAR: Forecast a model from the fable package

Description

Usage

Value

Arguments

Examples