Hw: A constructor for a Holt-Winters state-space model.

In bayesforecast: Bayesian Time Series Modeling with Stan

A constructor for a Holt-Winters state-space model.

Description

Constructor of the ets("A","A","A") object for Bayesian estimation in Stan.

Usage

Hw(
  ts,
  damped = FALSE,
  xreg = NULL,
  period = 0,
  genT = FALSE,
  series.name = NULL
)

Arguments

ts	a numeric or ts object with the univariate time series.
damped	a boolean value to specify a damped trend local level model. By default, damped = FALSE. If trend option is FALSE then damped is FALSE automatically.
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.
period	an integer specifying the periodicity of the time series by default the value frequency(ts) is used.
genT	a boolean value to specify for a generalized t-student SSM model.
series.name	an optional string vector with the time series names.

Details

The genT = TRUE option generates a t-student innovations SSM model. For a detailed explanation, check Ardia (2010); or Fonseca, et. al (2019).

The default priors used in a ssm( ) model are:

• level ~ normal(0,0.5)

• Trend ~ normal(0,0.5)

• damped~ normal(0,0.5)

• Seasonal ~ normal(0,0.5)

• sigma0 ~ t-student(0,1,7)

• level1 ~ normal(0,1)

• trend1 ~ normal(0,1)

• seasonal1 ~ normal(0,1)

• dfv ~ gamma(2,0.1)

• breg ~ t-student(0,2.5,6)

For changing the default prior use the function set_prior().

Value

The function returns a list with the data for running stan() function of rstan package.

Author(s)

Asael Alonzo Matamoros.

References

Fonseca, T. and Cequeira, V. and Migon, H. and Torres, C. (2019). The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations. arXiv doi: arXiv: 1910.01398.

See Also

Sarima, auto.arima, and set_prior. garch

Examples

mod1 = Hw(ipc)

# Declaring a Holt Winters damped trend model for the ipc data.
mod2 = Hw(ipc,damped = TRUE)

# Declaring an additive Holt-Winters model for the birth data
mod3 = Hw(birth,damped = FALSE)

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