stsm_init_pars {autostsm} | R Documentation |
Get initial parameter estimates for estimation
Description
Get initial parameter estimates for estimation
Usage
stsm_init_pars(
y,
freq,
trend,
cycle,
decomp = "",
seasons = NULL,
prior = NULL,
sig_level = 0.01,
arma = c(p = NA, q = NA),
exo = NULL,
state_eqns = NULL,
interpolate = NA,
interpolate_method = NA
)
Arguments
y |
an object created from stsm_detect_frequency |
freq |
Frequency of the data |
trend |
Trend specification ("random-walk", "random-walk-drift", "double-random-walk", "random-walk2"). |
cycle |
The period for the longer-term cycle |
decomp |
Decomposition model ("tend-cycle-seasonal", "trend-seasonal", "trend-cycle", "trend-noise") |
seasons |
The seasonal lengths to split the seasonality into |
prior |
A data table created by stsm_prior |
sig_level |
Significance level for statistical tests |
arma |
Named vector with values for p and q corresponding to the ARMA(p,q) specification if |
exo |
Matrix of exogenous variables. Can be used to specify regression effects or other seasonal effects like holidays, etc. |
state_eqns |
Character vector of equations to apply exo_state to the unobserved components. If left as the default, then all variables in exo_state will be applied to all the unobserved components. The equations should look like: "trend ~ var - 1", "drift ~ var - 1", "cycle ~ var - 1", "seasonal ~ var - 1". If only some equations are specified, it will be assumed that the exogenous data will be applied to only those specified equations. |
interpolate |
Character string giving frequency to interpolate to: i.e. "quarterly", "monthly", "weekly", "daily" cycle is set to 'arma'. If NA, then will auto-select the order. |
interpolate_method |
Character string giving the interpolation method: |
Value
named vector containing the initial parameter estimates for estimation