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>Construct a dataframe of holiday dates.
</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>dates</code></td>
<td>
<p>Vector with dates used for computing seasonality.</p>
</td>
</tr>
</table>

<p>A dataframe of holiday dates, in holiday dataframe format used in
initialization.
</p>

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

construct_holiday_dataframe

Description

Usage

Arguments

Value

prophet

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`m`	Prophet object.
`dates`	Vector with dates used for computing seasonality.