tsprofile.tsissm.estimate {tsissm} | R Documentation |
Model Simulation Based Profiling
Description
Profiling of model dynamics using simulation/estimation/prediction.
Usage
## S3 method for class 'tsissm.estimate'
tsprofile(
object,
h = 1,
nsim = 100,
seed = NULL,
solver = "nloptr",
control = NULL,
trace = FALSE,
...
)
Arguments
object |
an object of class “tsissm.estimate”. |
h |
the forecast horizon on which to evaluate performance metrics. |
nsim |
the number of paths to generate. |
seed |
an object specifying if and how the random number generator should be initialized. See the simulate documentation for more details. |
solver |
only “nlortr” currently available. |
control |
optional control parameters.
The user is expected to have set up appropriate handlers for this using
the |
trace |
whether to show the progress bar and additionally output verbose messages. |
... |
not currently used. |
Details
The function profiles an estimated model by simulating and then estimating multiple paths from the assumed DGP while leaving h values out for prediction evaluation. Each simulated path is equal to the size of the original dataset plus h additional values, and initialized with the initial state vector from the model. The resulting output contains the distribution of the MAPE, percent bias (BIAS) and mean squared log relative error (MSLRE) per horizon h. Since these matrices are of class “tsmodel.distribution” they can be readily plotted with the special purpose “plot” function for this class from the “tsmethods” package. Additionally, a data.table matrix is also returned with the distribution of the coefficients from each path estimation.
Value
An object of class “tsissm.profile”.
Note
The function can use parallel functionality as long as the user has set
up a plan
using the future package.
The simulated states are checked for positivity and any paths which have negative
values are excluded. If more than half of the paths have negative values then
an error is raised and the function will stop.