forecast.varstan: Forecasting 'varstan' objects.

In bayesforecast: Bayesian Time Series Modeling with Stan

Forecasting varstan objects.

Description

forecast is a generic function for forecasting from time series or varstan models. The function invokes particular methods which depend on the class of the first argument.

Usage

## S3 method for class 'varstan'
forecast(
  object,
  h = 10,
  probs = c(0.8, 0.9),
  xreg = NULL,
  robust = FALSE,
  draws = 1000,
  seed = NULL,
  ...
)

Arguments

object	a time series or varstan model for which forecasts are required.
h	an integer with the number of periods for forecasting.
probs	a numerical vector p \in (0,1) indicating the desired probability mass to include in the intervals. The default is to report 90% and 80% intervals (level=c(0.8,0.9)).
xreg	Optionally, a numerical matrix of external regressors, which must have the same number of rows as ts. It should not be a data frame.
robust	a boolean for obtain the robust estimation. The default
draws	an integer indicating the number of draws to return. The default number of draws is 1000.
seed	An optional seed to use.
...	Further arguments passed to posterior_predict.

Details

If model = NULL,the function forecast.ts makes forecasts using ets models (if the data are non-seasonal or the seasonal period is 12 or less).

If model is not NULL, forecast.ts will apply the model to the object time series, and then generate forecasts accordingly.

Value

An object of class "forecast".

The function summary is used to obtain and print a summary of the results, while the function plot produces a plot of the forecasts and prediction intervals.

The generic accessors functions fitted.values and residuals extract various useful features of the value returned by forecast$model.

An object of class "forecast" is a list usually containing at least the following elements:

model	A list containing information about the fitted model
method	The name of the forecasting method as a character string
mean	Point forecasts as a time series
lower	Lower limits for prediction intervals
upper	Upper limits for prediction intervals
level	The confidence values associated with the prediction intervals
x	The original time series (either object itself or the time series used to create the model stored as object).
residuals	Residuals from the fitted model. For models with additive errors, the residuals will be x minus the fitted values.
fitted	Fitted values (one-step forecasts)

Author(s)

Asael Alonzo Matamoros.

See Also

The "forecast" methods of the forecast package.

Examples

 fit = auto.sarima(ts = birth,iter = 500,chains = 1)
 fc = forecast(fit,h = 12)

bayesforecast documentation built on June 8, 2025, 10:42 a.m.