processNOCdata: Simulate NOC Observations from a Single-State or Multi-State Process

Description

This function generates data under normal operating conditions from a single-state or multi-state process model.

Usage

processNOCdata(
  startTime = "2015-05-16 10:00:00 CST",
  period = 7 * 24 * 60,
  stateDuration = 60,
  increment = "min",
  multiState = TRUE,
  autocorellation = 0.75,
  tLower = 0.01,
  tUpper = 2,
  errVar = 0.01
)

Arguments

startTime

a POSIXct object specifying the day and time for the starting observation.

period

The observation cycle length. Defaults to one week's worth of minute-level observations (10,080 observations).

stateDuration

The number of observations generated during a stay in each state. Defaults to 60.

increment

The time-sequence base increment. See "Deatails" of the seq.POSIXt() function options. Defaults to "min" for minutes.

multiState

Should the observations be generated from a multi-state process? Defaults to TRUE.

autocorellation

The autocorrelation parameter. Must be less than 1 in absolute value, or the process generated will be nonstationary. Defaults to 0.75 in accordance to Kazor et al (2016).

tLower

Lower bound of the latent $t$ variable. Defaults to 0.01.

tUpper

Upper bound of the latent $t$ variable. Defaults to 2.

errVar

Error variance of the normal white noise process on the feature variables.

Value

An data frame with the following information:

dateTime -- A POSIXct column of times starting at the user-defined `startTime` argument, length given by the `period` argument, and spacing given by the `increment` argument. For example, if the starting value is "2016-01-10", period is 10080, and the incrementation is in minutes, then this sequence will be one week's worth of observations recorded every minute from midnight on the tenth of January.
state -- An integer column of all 1's (when the `multiState` argument is FALSE), or a column of the state values (1, 2 or 3).
x -- A double column of generated values for the first feature.
y -- A double column of generated values for the second feature.
z -- A double column of generated values for the third feature.

Details

This function randomly generates a non-stationary (sinusoidal) and autocorrelated latent variable t with lower and upper bounds given by the arguments "tLower" and "tUpper", respectively, with autocorrelation governed by the "autocorrelation" argument. Necessarily, this coefficient must be less than 1 in absolute value, otherwise the latent variable will be unbounded. Next, this function draws a realization of this random variable t and calculates three functions of it, then jitters these functions with a normal white noise variable (with variance set by "errVar"). These three functions are:

x : x(t) = t + error
y : y(t) = t ^ 2 - 3t + error
z : z(t) = -t ^ 3 + 3t ^ 2 + error

This function is called by the mspProcessData() function. See ?mspProcessData for more details.

Examples

Run this code

# NOT RUN {
processNOCdata()
# }