smooth (version 4.3.1)

sim.sma: Simulate Simple Moving Average

Description

Function generates data using SMA in a Single Source of Error state space model as a data generating process.

Usage

sim.sma(order = NULL, obs = 10, nsim = 1, frequency = 1,
  initial = NULL, randomizer = c("rnorm", "rt", "rlaplace", "rs"),
  probability = 1, ...)

Value

List of the following values is returned:

• model - Name of SMA 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.

• initial - Vector (or matrix) of initial values.

• 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 - Log-likelihood of the constructed model.

Details

For the information about the function, see the vignette: vignette("simulate","smooth")

References

• Svetunkov, I., 2023. Smooth Forecasting with the Smooth Package in R. arXiv. tools:::Rd_expr_doi("10.48550/arXiv.2301.01790")

• Snyder, R. D., 1985. Recursive Estimation of Dynamic Linear Models. Journal of the Royal Statistical Society, Series B (Methodological) 47 (2), 272-276.

• Hyndman, R.J., Koehler, A.B., Ord, J.K., and Snyder, R.D. (2008) Forecasting with exponential smoothing: the state space approach, Springer-Verlag. tools:::Rd_expr_doi("10.1007/978-3-540-71918-2").

See Also

es, ts, Distributions

Examples

# Create 40 observations of quarterly data using AAA model with errors from normal distribution
sma10 <- sim.sma(order=10,frequency=4,obs=40,randomizer="rnorm",mean=0,sd=100)