Description Usage Arguments Details Value Author(s) References See Also Examples
Function generates data using GUM with Single Source of Error as a data generating process.
1 2 3 4 
orders 
Order of the model. Specified as vector of number of states
with different lags. For example, 
lags 
Defines lags for the corresponding orders. If, for example,

obs 
Number of observations in each generated time series. 
nsim 
Number of series to generate (number of simulations to do). 
frequency 
Frequency of generated data. In cases of seasonal models must be greater than 1. 
measurement 
Measurement vector w. If 
transition 
Transition matrix F. Can be provided as a vector.
Matrix will be formed using the default 
persistence 
Persistence vector g, containing smoothing
parameters. If 
initial 
Vector of initial values for state matrix. If 
randomizer 
Type of random number generator function used for error
term. Defaults are: 
probability 
Probability of occurrence, used for intermittent data generation. This can be a vector, implying that probability varies in time (in TSB or Croston style). 
... 
Additional parameters passed to the chosen randomizer. All the
parameters should be passed in the order they are used in chosen randomizer.
For example, passing just 
For the information about the function, see the vignette:
vignette("simulate","smooth")
List of the following values is returned:
model
 Name of GUM model.
measurement
 Matrix w.
transition
 Matrix F.
persistence
 Persistence vector. This is the place, where
smoothing parameters live.
initial
 Initial values of GUM in a form of matrix. If nsim>1
,
then this is an array.
data
 Time series vector (or matrix if nsim>1
) of the generated
series.
states
 Matrix (or array if nsim>1
) of states. States are in
columns, time is in rows.
residuals
 Error terms used in the simulation. Either vector or matrix,
depending on nsim
.
occurrence
 Values of occurrence variable. Once again, can be either
a vector or a matrix...
logLik
 Loglikelihood of the constructed model.
Ivan Svetunkov, ivan@svetunkov.ru
Svetunkov I. (2015  Inf) "smooth" package for R  series of posts about the underlying models and how to use them: https://forecasting.svetunkov.ru/en/tag/smooth/.
Svetunkov I. (2017). Statistical models underlying functions of 'smooth' package for R. Working Paper of Department of Management Science, Lancaster University 2017:1, 152.
sim.es, sim.ssarima,
sim.ces, gum, Distributions
1 2 3 4 5 6 7 8 9  # Create 120 observations from GUM(1[1]). Generate 100 time series of this kind.
x < sim.gum(orders=c(1),lags=c(1),obs=120,nsim=100)
# Generate similar thing for seasonal series of GUM(1[1],1[4]])
x < sim.gum(orders=c(1,1),lags=c(1,4),frequency=4,obs=80,nsim=100,transition=c(1,0,0.9,0.9))
# Estimate model and then generate 10 time series from it
ourModel < gum(rnorm(100,100,5))
simulate(ourModel,nsim=10)

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