Description Usage Arguments Details Value Author(s) References See Also Examples
Function generates data using ETS with Single Source of Error as a data generating process.
1 2 3 4 
model 
Type of ETS model according to [Hyndman et. al., 2008]
taxonomy. Can consist of 3 or 4 chars: 
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. 
persistence 
Persistence vector, which includes all the smoothing
parameters. Must correspond to the chosen model. The maximum length is 3:
level, trend and seasonal smoothing parameters. If 
phi 
Value of damping parameter. If trend is not chosen in the model, the parameter is ignored. 
initial 
Vector of initial states of level and trend. The maximum
length is 2. If 
initialSeason 
Vector of initial states for seasonal coefficients.
Should have length equal to 
bounds 
Type of bounds to use for persistence vector if values are
generated. 
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 ETS model.
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.
persistence
 Vector (or matrix if nsim>1
) of smoothing
parameters used in the simulation.
phi
 Value of damping parameter used in time series generation.
initial
 Vector (or matrix) of initial values.
initialSeason
 Vector (or matrix) of initial seasonal coefficients.
probability
 vector of probabilities used in the simulation.
intermittent
 type of the intermittent model used.
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
Snyder, R. D., 1985. Recursive Estimation of Dynamic Linear Models. Journal of the Royal Statistical Society, Series B (Methodological) 47 (2), 272276.
Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, SpringerVerlag. doi: 10.1007/9783540719182.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  # Create 40 observations of quarterly data using AAA model with errors from normal distribution
ETSAAA < sim.es(model="AAA",frequency=4,obs=40,randomizer="rnorm",mean=0,sd=100)
# Create 50 series of quarterly data using AAA model
# with 40 observations each with errors from normal distribution
ETSAAA < sim.es(model="AAA",frequency=4,obs=40,randomizer="rnorm",mean=0,sd=100,nsim=50)
# Create 50 series of quarterly data using AAdA model
# with 40 observations each with errors from normal distribution
# and smoothing parameters lying in the "admissible" range.
ETSAAA < sim.es(model="AAA",phi=0.9,frequency=4,obs=40,bounds="admissible",
randomizer="rnorm",mean=0,sd=100,nsim=50)
# Create 60 observations of monthly data using ANN model
# with errors from beta distribution
ETSANN < sim.es(model="ANN",persistence=c(1.5),frequency=12,obs=60,
randomizer="rbeta",shape1=1.5,shape2=1.5)
plot(ETSANN$states)
# Create 60 observations of monthly data using MAM model
# with errors from uniform distribution
ETSMAM < sim.es(model="MAM",persistence=c(0.3,0.2,0.1),initial=c(2000,50),
phi=0.8,frequency=12,obs=60,randomizer="runif",min=0.5,max=0.5)
# Create 80 observations of quarterly data using MMM model
# with predefined initial values and errors from the normal distribution
ETSMMM < sim.es(model="MMM",persistence=c(0.1,0.1,0.1),initial=c(2000,1),
initialSeason=c(1.1,1.05,0.9,.95),frequency=4,obs=80,mean=0,sd=0.01)
# Generate intermittent data using AAdN
iETSAAdN < sim.es("AAdN",obs=30,frequency=1,probability=0.1,initial=c(3,0),phi=0.8)
# Generate iETS(MNN) with TSB style probabilities
oETSMNN < sim.oes("MNN",obs=50,occurrence="d",persistence=0.2,initial=1,
randomizer="rlnorm",meanlog=0,sdlog=0.3)
iETSMNN < sim.es("MNN",obs=50,frequency=12,persistence=0.2,initial=4,
probability=oETSMNN$probability)

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