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gen_lts

Generate a Latent Time Series Object Based on a Model

Description

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

Usage

gen_lts(n, model, start = 0, end = NULL, freq = 1, unit_ts = NULL,
  unit_time = NULL, name_ts = NULL, name_time = NULL,
  process = NULL)

Arguments

n

An interger indicating the amount of observations generated in this function.
model

A ts.model or simts object containing one of the allowed models.
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/frequency at which the time series is sampled. The default value is 1.
unit_ts

A string that contains the unit of measure of the time series. The default value is NULL.
unit_time

A string that contains the unit of measure of the time. The default value is NULL.
name_ts

A string that provides an identifier for the time series data. Default value is NULL.
name_time

A string that provides an identifier for the time. Default value is NULL.
process

A vector that contains model names of each column in the data object where the last name is the sum of the previous names.

Details

This function accepts either a ts.model object (e.g. AR1(phi = .3, sigma2 =1) + WN(sigma2 = 1)) or a simts 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 the sampling frequency/rate.

unit

A string reporting the unit of measurement.

name

Name of the generated dataset.

process

A vector that contains model names of decomposed and combined processes

Author(s)

James Balamuta, Wenchao Yang, and Justin Lee

Examples

# AR
set.seed(1336)
model = AR1(phi = .99, sigma2 = 1) + WN(sigma2 = 1)
test = gen_lts(1000, model)
plot(test)
simts

Time Series Analysis Tools

v0.1.1
AGPL-3 | file LICENSE
Authors
Stéphane Guerrier [aut, cre, cph], James Balamuta [aut, cph], Roberto Molinari [aut, cph], Justin Lee [aut], Yuming Zhang [aut], Wenchao Yang [ctb], Nathanael Claussen [ctb], Yunxiang Zhang [ctb], Christian Gunning [cph], Romain Francois [cph], Ross Ihaka [cph], R Core Team [cph]
Initial release
2019-07-21

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