Hw {bayesforecast} | R Documentation |
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, |
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)