Description Usage Arguments Details Value Author(s) References See Also Examples
Function generates data using CES with Single Source of Error as a data generating process.
1 2 3 
seasonality 
The type of seasonality used in CES. Can be: 
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. 
a 
First complex smoothing parameter. Should be a complex number. NOTE! CES is very sensitive to a and b values so it is advised to use values from previously estimated model. 
b 
Second complex smoothing parameter. Can be real if

initial 
A matrix with initial values for CES. In case with

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 CES model.
a
 Value of complex smoothing parameter a. If nsim>1
, then
this is a vector.
b
 Value of complex smoothing parameter b. If seasonality="n"
or seasonality="s"
, then this is equal to NULL. If nsim>1
,
then this is a vector.
initial
 Initial values of CES 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., Kourentzes, N. (February 2015). Complex exponential smoothing. Working Paper of Department of Management Science, Lancaster University 2015:1, 131.
Svetunkov I., Kourentzes N. (2017) Complex Exponential Smoothing for Time Series Forecasting. Not yet published.
sim.es, sim.ssarima,
ces, Distributions
1 2 3 4 5 6 7 8 9  # Create 120 observations from CES(n). Generate 100 time series of this kind.
x < sim.ces("n",obs=120,nsim=100)
# Generate similar thing for seasonal series of CES(s)_4
x < sim.ces("s",frequency=4,obs=80,nsim=100)
# Estimate model and then generate 10 time series from it
ourModel < ces(rnorm(100,100,5))
simulate(ourModel,nsim=10)

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