sample_model

Simulate observations from the extrapolated generative model.

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

sample_model function

<dl><dt>m</dt>
<dd>Prophet object.</dd>
<dt>df</dt>
<dd>Prediction dataframe.</dd>
<dt>seasonal.features</dt>
<dd>Data frame of seasonal features</dd>
<dt>iteration</dt>
<dd>Int sampling iteration to use parameters from.</dd>
<dt>s_a</dt>
<dd>Indicator vector for additive components</dd>
<dt>s_m</dt>
<dd>Indicator vector for multiplicative components</dd></dl>

Arguments

Simulate observations from the extrapolated generative model. — sample_model

<dl>

<dt>m</dt>
<dd>Prophet object.</dd>


<dt>df</dt>
<dd>Prediction dataframe.</dd>


<dt>seasonal.features</dt>
<dd>Data frame of seasonal features</dd>


<dt>iteration</dt>
<dd>Int sampling iteration to use parameters from.</dd>


<dt>s_a</dt>
<dd>Indicator vector for additive components</dd>


<dt>s_m</dt>
<dd>Indicator vector for multiplicative components</dd>

</dl>

sample_model: Simulate observations from the extrapolated generative model.

Description

Usage

Value

Arguments