.stan_args

Gives Stan arguments the appropriate names depending on the chosen Stan backend.

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

.stan_args function

<dl><dt>model</dt>
<dd>Model object.</dd>
<dt>dat</dt>
<dd>List containing data to use in fitting.</dd>
<dt>stan_init</dt>
<dd>Function to initialize parameters for stan fit.</dd>
<dt>backend</dt>
<dd>"rstan" or "cmdstanr".</dd>
<dt>type</dt>
<dd>"mcmc" or "optimize".</dd>
<dt>mcmc_samples</dt>
<dd>Integer, if greater than 0, will do full Bayesian
inference with the specified number of MCMC samples. If 0, will do MAP
estimation.</dd></dl>

Arguments

Gives Stan arguments the appropriate names depending on the chosen Stan backend. — .stan_args

<dl>

<dt>model</dt>
<dd>Model object.</dd>


<dt>dat</dt>
<dd>List containing data to use in fitting.</dd>


<dt>stan_init</dt>
<dd>Function to initialize parameters for stan fit.</dd>


<dt>backend</dt>
<dd>"rstan" or "cmdstanr".</dd>


<dt>type</dt>
<dd>"mcmc" or "optimize".</dd>


<dt>mcmc_samples</dt>
<dd>Integer, if greater than 0, will do full Bayesian
inference with the specified number of MCMC samples. If 0, will do MAP
estimation.</dd>

</dl>

.stan_args: Gives Stan arguments the appropriate names depending on the chosen Stan backend.

Description

Usage

Value

Arguments