interpolate.ARIMA

Applies a model-specific estimation technique to predict the values of missing values in a <code>tsibble</code>, and replace them.

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

interpolate.ARIMA 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>...</dt>
<dd>Other arguments passed to methods</dd></dl>

Arguments

Interpolate missing values from a fable model — interpolate.ARIMA

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

</dl>

interpolate.ARIMA: Interpolate missing values from a fable model

Description

Usage

Value

Arguments

Examples