ssm: A constructor for a Additive linear State space model.

In bayesforecast: Bayesian Time Series Modeling with Stan

A constructor for a Additive linear State space model.

Description

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

Usage

ssm(
  ts,
  trend = FALSE,
  damped = FALSE,
  seasonal = FALSE,
  xreg = NULL,
  period = 0,
  genT = FALSE,
  series.name = NULL
)

Arguments

ts	a numeric or ts object with the univariate time series.
trend	a bool value to specify a trend local level model. By default, trend = FALSE.
damped	a bool value to specify a damped trend local level model. By default, damped = FALSE. If trend = FALSE then damped = FALSE automatically.
seasonal	a bool value to specify a seasonal local level model. By default seasonal = FALSE.
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 bool value to specify for a generalized t-student SSM model.
series.name	an optional string vector with the time series names.

Details

By default the ssm() function generates a local level ets("A","N","N"), or exponential smoothing model. If trend = TRUE, then the model transforms into a local trend, ets("A","A","N") or Holt model from the nforecast package. For damped trend models set damped = TRUE. When seasonal = TRUE, the model becomes a seasonal local level or ⁠ets("A","N","A")`` model from the \pkg{forecast} package. Finally, a Holt-Winters method or ⁠ets("A","A","A")',is whenever both Trend and seasonal options are TRUE.

The genT = TRUE defines a t-student innovations SSM model. Check, Ardia (2010)) and Fonseca, et. al (2019) for more details.

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, set_prior, and garch.

Examples

mod1 = ssm(ipc)

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

# Declaring an additive Holt-Winters model for the birth data
mod3 = ssm(birth,trend = TRUE,damped = TRUE,seasonal = TRUE)

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