demo.lts: Generate a Demo about the Latent Time Series
In gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Author(s) Examples

Creates a time series based on the supplied model, then generate a demo about its latent structure

1 2	demo.lts(model, N = 1000, start = 0, end = NULL, freq = 1, unit = NULL, name = NULL, process = NULL, ...)

`model`	A `ts.model` or `gmwm` object containing one of the allowed models.
`N`	An `interger` indicating the amount of observations generated in this function.
`start`	A `numeric` that provides the time of the first observation.
`end`	A `numeric` that provides the time of the last observation.
`freq`	A `numeric` that provides the rate of samples. Default value is 1.
`unit`	A `string` that contains the unit expression of the frequency. Default value is `NULL`.
`name`	A `string` that provides an identifier to the data. Default value is `NULL`.
`process`	A `vector` that contains model names of decomposed and combined processes.
`...`	Additional parameters passed to `autoplot.lts`

This function accepts either a ts.model object (e.g. AR1(phi = .3, sigma2 =1) + WN(sigma2 = 1)) or a gmwm object.

Wenchao

# AR
set.seed(1336)
model = AR1(phi = .99, sigma = 1) + WN(sigma2=1)
demo.lts(model)

# Modify the graph aesthetics
demo.lts(model, N = 100, line.color = c('blue', 'green', 'black'), 
         point.size = c(1,1,1), process = c('AR1', 'WN', 'Sum'))