Description Usage Arguments Details Value Author(s) References See Also Examples
Function generates data using SSARIMA with Single Source of Error as a data generating process.
1 2 3 4 
orders 
List of orders, containing vector variables 
lags 
Defines lags for the corresponding orders (see examples above).
The length of 
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. 
AR 
Vector or matrix of AR parameters. The order of parameters should be lagwise. This means that first all the AR parameters of the firs lag should be passed, then for the second etc. AR of another ssarima can be passed here. 
MA 
Vector or matrix of MA parameters. The order of parameters should be lagwise. This means that first all the MA parameters of the firs lag should be passed, then for the second etc. MA of another ssarima can be passed here. 
constant 
If 
initial 
Vector of initial values for state matrix. If 
bounds 
Type of bounds to use for AR and MA 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 SSARIMA model.
AR
 Value of AR parameters. If nsim>1
, then this is a
matrix.
MA
 Value of MA parameters. If nsim>1
, then this is a
matrix.
constant
 Value of constant term. If nsim>1
, then this
is a vector.
initial
 Initial values of SSARIMA. If nsim>1
, then this
is a matrix.
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
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.
Svetunkov, I., & Boylan, J. E. (2019). Statespace ARIMA for supplychain forecasting. International Journal of Production Research, 0(0), 1–10. doi: 10.1080/00207543.2019.1600764
sim.es, ssarima,
Distributions, orders
1 2 3 4 5 6 7 8 9 10  # Create 120 observations from ARIMA(1,1,1) with drift. Generate 100 time series of this kind.
x < sim.ssarima(ar.orders=1,i.orders=1,ma.orders=1,obs=120,nsim=100,constant=TRUE)
# Generate similar thing for seasonal series of SARIMA(1,1,1)(0,0,2)_4
x < sim.ssarima(ar.orders=c(1,0),i.orders=c(1,0),ma.orders=c(1,2),lags=c(1,4),
frequency=4,obs=80,nsim=100,constant=FALSE)
# Generate 10 series of high frequency data from SARIMA(1,0,2)_1(0,1,1)_7(1,0,1)_30
x < sim.ssarima(ar.orders=c(1,0,1),i.orders=c(0,1,0),ma.orders=c(2,1,1),lags=c(1,7,30),
obs=360,nsim=10)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.