# simssm

##### Simulation by Gaussian State Space Model

Simulate time series by Gaussian State Space Model.

- Keywords
- ts

##### Usage

```
simssm(n = 200, trend = NULL, seasonal.order = 0, seasonal = NULL,
arcoef = NULL, ar = NULL, tau1 = NULL, tau2 = NULL, tau3 = NULL,
sigma2 = 1.0, seed = NULL, plot = TRUE, …)
```

##### Arguments

- n
the number of simulated data.

- trend
initial values of trend component of length at most 2.

- seasonal.order
seasonal order. (0 or 1)

- seasonal
if

`seasonal.order`

> 0, initial values of seasonal component of length \(p-1\), where \(p\) is the number of season in one period.- arcoef
AR coefficients.

- ar
initial values of AR component.

- tau1
variance of trend component model.

- tau2
variance of AR component model.

- tau3
variance of seasonal component model.

- sigma2
variance of the observation noise.

- seed
arbitrary positive integer to generate a sequence of uniform random numbers. The default seed is based on the current time.

- plot
logical. If

`TRUE`

(default), simulated data are plotted.- …
further arguments to be passed to

`plot.simulate`

.

##### Value

An object of class `"simulate"`

, giving simulated data of Gaussian state
space model.

##### References

Kitagawa, G. (2010)
*Introduction to Time Series Modeling*. Chapman & Hall/CRC.

##### Examples

```
# NOT RUN {
# BLSALLFOOD data
data(BLSALLFOOD)
m1 <- 2; m2 <- 1; m3 <- 2
z <- season(BLSALLFOOD, trend.order = m1, seasonal.order = m2, ar.order = m3)
nl <- length(BLSALLFOOD)
trend <- z$trend[m1:1]
arcoef <- z$arcoef
period <- 12
seasonal <- z$seasonal[(period-1):1]
ar <- z$ar[m3:1]
tau1 <- z$tau2[1]
tau2 <- z$tau2[2]
tau3 <- z$tau2[3]
simssm(n = nl, trend, seasonal.order = m2, seasonal, arcoef, ar, tau1, tau2,
tau3, sigma2 = z$sigma2, seed = 333)
# }
```

*Documentation reproduced from package TSSS, version 1.2.3, License: GPL (>= 2)*