Description Usage Arguments Details Value Author(s) See Also Examples
refit
function generates the parameters based on the values in the provided
object and then reapplies the same model with those parameters to the data, getting
the fitted paths and updated states. reforecast
function uses those values
in order to produce forecasts for the h
steps ahead.
1 2 3 4 5 6 
object 
Model estimated using one of the functions of smooth package. 
nsim 
Number of paths to generate (number of simulations to do). 
bootstrap 
The logical, which determines, whether to use bootstrap for the covariance matrix of parameters or not. 
... 
Other parameters passed to 
h 
Forecast horizon. 
newdata 
The new data needed in order to produce forecasts. 
occurrence 
The vector containing the future occurrence variable (values in [0,1]), if it is known. 
interval 
What type of mechanism to use for interval construction. The options
include 
level 
Confidence level. Defines width of prediction interval. 
side 
Defines, whether to provide 
cumulative 
If 
The main motivation of the function is to take the randomness due to the insample estimation of parameters into account when fitting the model and to propagate this randomness to the forecasts. The methods can be considered as a special case of recursive bootstrap.
refit()
returns object of the class "refit", which contains:
states
 The array of states of the model;
fitted
 The matrix with fitted values, where columns correspond
to different paths;
transition
 The array of transition matrices;
measurement
 The array of measurement matrices;
persistence
 The matrix of persistence vectors (paths in columns);
profile
 The array of profiles obtained by the end of each fit.
reforecast()
returns the object of the class forecast.smooth,
which contains in addition to the standard list the variable paths
 all
simulated trajectories with h in rows, simulated future paths for each state in
columns and different states (obtained from refit()
function) in the
third dimension.
Ivan Svetunkov, ivan@svetunkov.ru
forecast.smooth
1 2 3 4 5 6 7 8  x < rnorm(100,0,1)
# Just as example. orders and lags do not return anything for ces() and es(). But modelType() does.
ourModel < adam(x, "ANN")
refittedModel < refit(ourModel, nsim=50)
plot(refittedModel)
ourForecast < reforecast(ourModel, nsim=50)

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