Generate Latent Time Series Object Based on Model

Description

Create a lts object based on a supplied time series model.

Usage

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

Arguments

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.

Details

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

Value

A lts object with the following attributes:

start

The time of the first observation

end

The time of the last observation

freq

Numeric representation of frequency

unit

String representation of the unit

name

Name of the dataset

process

A vector that contains model names of decomposed and combined processes

Author(s)

JJB, Wenchao

Examples

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

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