Implements a procedure for forecasting time series data based on
an additive model where non-linear trends are fit with yearly, weekly, and
daily seasonality, plus holiday effects. It works best with time series
that have strong seasonal effects and several seasons of historical data.
Prophet is robust to missing data and shifts in the trend, and typically
handles outliers well.

Automatic Forecasting Procedure

package-ubuntu-20.04-r-4.0.5#cran#prophet-1.0

<p>Prophet posterior predictive samples.
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>m</code></td>
<td>
<p>Prophet object.</p>
</td>
</tr>
<tr valign="top">
<td><code>df</code></td>
<td>
<p>Prediction dataframe.</p>
</td>
</tr>
</table>

<p>List with posterior predictive samples for the forecast yhat and
for the trend component.
</p>

package-ubuntu-20.04-r-4.0.5#cran#prophet-1.0#docs#sample_posterior_predictive

sample_posterior_predictive

Description

Usage

Arguments

Value

prophet

We don't support your browser anymore

`m`	Prophet object.
`df`	Prediction dataframe.