mdape

internal

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.

Cuong Duong

prophet

Automatic Forecasting Procedure

Sean Taylor

Ben Letham

mdape function

<dl><dt>df</dt>
<dd>Cross-validation results dataframe.</dd>
<dt>w</dt>
<dd>Aggregation window size.</dd></dl>

Arguments

Median absolute percent error — mdape

<dl>

<dt>df</dt>
<dd>Cross-validation results dataframe.</dd>


<dt>w</dt>
<dd>Aggregation window size.</dd>

</dl>

mdape: Median absolute percent error

Description

Usage

Value

Arguments