StateSpaceForecast {statespacer} | R Documentation |
Produces forecasts using a fitted State Space Model.
StateSpaceForecast( fit, addvar_list_fc = NULL, level_addvar_list_fc = NULL, self_spec_list_fc = NULL, forecast_period = 1 )
fit |
A list containing the specifications of the State Space Model, as
returned by |
addvar_list_fc |
A list containing the explanatory variables for each
of the dependent variables. The list should contain p (number of dependent
variables) elements. Each element of the list should be a
|
level_addvar_list_fc |
A list containing the explanatory variables
for each of the dependent variables. The list should contain p
(number of dependent variables) elements. Each element of the list should
be a |
self_spec_list_fc |
A list containing the specification of the self
specified component. Does not have to be specified if it does not differ
from |
forecast_period |
Number of time steps to forecast ahead. |
A list containing the forecasts and corresponding uncertainties. In addition, it returns the components of the forecasts, as specified by the State Space model.
Dylan Beijers, dylanbeijers@gmail.com
Durbin J, Koopman SJ (2012). Time series analysis by state space methods. Oxford university press.
# Fits a local level model for the Nile data library(datasets) y <- matrix(Nile) fit <- StateSpaceFit(initial = 1, y = y / 100, local_level_ind = TRUE) # Obtain forecasts for 10 steps ahead using the fitted model fc <- StateSpaceForecast(fit, forecast_period = 10) # Plot the forecasts plot(1:10, fc$y_fc * 100, type = 'l')