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>Plot a custom seasonal component.
</p>

plot_seasonality(m, name, uncertainty = TRUE)

<table summary="R argblock">
<tr valign="top">
<td><code>m</code></td>
<td>
<p>Prophet model object.</p>
</td>
</tr>
<tr valign="top">
<td><code>name</code></td>
<td>
<p>String name of the seasonality.</p>
</td>
</tr>
<tr valign="top">
<td><code>uncertainty</code></td>
<td>
<p>Optional boolean to plot uncertainty intervals, which
will only be done if m$uncertainty.samples &gt; 0.</p>
</td>
</tr>
</table>

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

`m`	Prophet model object.
`name`	String name of the seasonality.
`uncertainty`	Optional boolean to plot uncertainty intervals, which will only be done if m$uncertainty.samples > 0.

plot_seasonality

Description

Usage

Arguments

Value

prophet

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