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

generate_cutoffs(df, horizon, initial, period)

<table summary="R argblock">
<tr valign="top">
<td><code>df</code></td>
<td>
<p>Dataframe with historical data.</p>
</td>
</tr>
<tr valign="top">
<td><code>horizon</code></td>
<td>
<p>timediff forecast horizon.</p>
</td>
</tr>
<tr valign="top">
<td><code>initial</code></td>
<td>
<p>timediff initial window.</p>
</td>
</tr>
<tr valign="top">
<td><code>period</code></td>
<td>
<p>timediff Simulated forecasts are done with this period.</p>
</td>
</tr>
</table>

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

generate_cutoffs

Description

Usage

Arguments

Value

prophet

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`df`	Dataframe with historical data.
`horizon`	timediff forecast horizon.
`initial`	timediff initial window.
`period`	timediff Simulated forecasts are done with this period.