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

Data frame with seasonality features.

make_seasonality_features(dates, period, series.order, prefix)

<table summary="R argblock">
<tr valign="top">
<td><code>dates</code></td>
<td>
Vector of dates.
</td>
</tr>
<tr valign="top">
<td><code>period</code></td>
<td>
Number of days of the period.
</td>
</tr>
<tr valign="top">
<td><code>series.order</code></td>
<td>
Number of components.
</td>
</tr>
<tr valign="top">
<td><code>prefix</code></td>
<td>
Column name prefix.
</td>
</tr>
</table>

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

make_seasonality_features

Description

Usage

Arguments

Value

prophet

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`dates`	Vector of dates.
`period`	Number of days of the period.
`series.order`	Number of components.
`prefix`	Column name prefix.