simssm: Simulation by Gaussian State Space Model

In TSSS: Time Series Analysis with State Space Model

Simulation by Gaussian State Space Model

Description

Simulate time series by Gaussian State Space Model.

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 data generated by simulation.
trend	initial values of trend component of length m1, where m1 is trend order (1, 2). If NULL (default), trend order is 0.
seasonal.order	order of seasonal component model (0, 1, 2).
seasonal	if seasonal.order > 0, initial values of seasonal component of length p-1, where p is period of one season.
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.
...	graphical arguments passed to plot.simulate.

Value

An object of class "simulate", giving simulated data of Gaussian state space model.

References

Kitagawa, G. (2020) Introduction to Time Series Modeling with Applications in R. Chapman & Hall/CRC.

Examples

# 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)

TSSS documentation built on Sept. 29, 2023, 9:07 a.m.