generate.model_mean

Simulates future paths from a dataset using a fitted model. Innovations are
sampled by the model's assumed error distribution. If <code>bootstrap</code> is <code>TRUE</code>,
innovations will be sampled from the model's residuals. If <code>new_data</code>
contains the <code>.innov</code> column, those values will be treated as innovations.

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

generate.model_mean function

<dl><dt>x</dt>
<dd>A fitted model.</dd>
<dt>new_data</dt>
<dd>A tsibble containing the time points and exogenous regressors to produce forecasts for.</dd>
<dt>bootstrap</dt>
<dd>If <code>TRUE</code>, then forecast distributions are computed using simulation with resampled errors.</dd>
<dt>...</dt>
<dd>Other arguments passed to methods</dd></dl>

Arguments

Generate new data from a fable model — generate.model_mean

<dl>

<dt>x</dt>
<dd>A fitted model.</dd>


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


<dt>bootstrap</dt>
<dd>If <code>TRUE</code>, then forecast distributions are computed using simulation with resampled errors.</dd>


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

</dl>

generate.model_mean: Generate new data from a fable model

Description

Usage

Arguments

See Also

Examples