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>Sets model scaling factors using df.
</p>

initialize_scales_fn(m, initialize_scales, df)

<table summary="R argblock">
<tr valign="top">
<td><code>m</code></td>
<td>
<p>Prophet object.</p>
</td>
</tr>
<tr valign="top">
<td><code>initialize_scales</code></td>
<td>
<p>Boolean set the scales or not.</p>
</td>
</tr>
<tr valign="top">
<td><code>df</code></td>
<td>
<p>Dataframe for setting scales.</p>
</td>
</tr>
</table>

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

initialize_scales_fn

Description

Usage

Arguments

Value

prophet

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`m`	Prophet object.
`initialize_scales`	Boolean set the scales or not.
`df`	Dataframe for setting scales.