Generate a Latent Time Series Object Based on a Model

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

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.

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

Details

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

Author

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)