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>Symmetric mean absolute percentage error
based on Chen and Yang (2004) formula
</p>

<table summary="R argblock">
<tr valign="top">
<td><code>df</code></td>
<td>
<p>Cross-validation results dataframe.</p>
</td>
</tr>
<tr valign="top">
<td><code>w</code></td>
<td>
<p>Aggregation window size.</p>
</td>
</tr>
</table>

<p>Array of symmetric mean absolute percent errors.
</p>

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

smape

Description

Usage

Arguments

Value

prophet

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`df`	Cross-validation results dataframe.
`w`	Aggregation window size.