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
)

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

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.

Author

James Balamuta, Wenchao Yang, and Justin Lee

Details

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

Examples

Run this code

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

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