simulate: Model Simulation

In tsmodels/tsets: Exponential Smoothing Models

Model Simulation

Description

Simulates paths from an ETS model with optional user specified values.

Usage

## S3 method for class 'tsets.estimate'
simulate(
  object,
  nsim = 1,
  seed = NULL,
  h = NULL,
  newxreg = NULL,
  sim_dates = NULL,
  bootstrap = FALSE,
  innov = NULL,
  sigma_scale = 1,
  pars = coef(object),
  ...
)

Arguments

object	an object of class “tsets.estimate”.
nsim	the number of paths per complete set of time steps (h).
seed	an object specifying if and how the random number generator should be initialized (‘seeded’). Either NULL or an integer that will be used in a call to set.seed before simulating the response vectors. If set, the value is saved as the "seed" attribute of the returned value. The default, NULL will not change the random generator state, and return .Random.seed as the “seed” attribute in the returned object.
h	the number of time steps to simulate paths for. If this is NULL, it will use the same number of periods as in the original series.
newxreg	an optional matrix of regressors to use for the simulation if xreg was used in the estimation. If NULL and the estimated object had regressors, and h was also set to NULL, then the original regressors will be used.
sim_dates	an optional vector of simulation dates equal to h. If NULL will use the implied periodicity of the data to generate a regular sequence of dates after the first available date in the data.
bootstrap	whether to bootstrap the innovations from the estimated object by re-sampling from the empirical distribution.
innov	an optional vector of uniform innovations which will be translated to regular innovations using the appropriate distribution quantile function and model standard deviation. The length of this vector should be equal to nsim x horizon.
sigma_scale	an optional scalar which will scale the standard deviation of the innovations (useful for profiling under different assumptions). This applies to all approaches (parametric, bootstrap and custom innovations supplied values).
pars	an optional named vector of model coefficients which override the estimated coefficients. No checking is currently performed on the adequacy of these coefficients.
...	not currently used.

Details

It is required that an initial object of class “tsets.estimate” be passed to this function, but the parameters and initial states can be overriden by passing them as named values in the ‘pars’ argument. The default is to initialize the states from the seed states used in the estimated object, with h equal to the length of the original series (default for NULL h). Innovations for the simulation can either be parametric (Normal for additive or truncated Normal for multiplicative error models), based on the estimated residuals (bootstrap argument) or use supplied set of uniform random numbers (which are then translated into Normal or truncated Normal using standard deviation equal to the model sigma and optionally scaled by sigma_scale).

Value

An object of class “tsets.simulate” with slots for the simulated series and states.

tsmodels/tsets documentation built on Oct. 8, 2022, 9:15 a.m.