simulation_master_list {knnwtsim}

simulation_master_list

Description

A list of 20 lists. Each of the 20 lists contains 31 items including 4 simulated time series. Each series contains an ARIMA component, a periodic component simulated using trig functions, a component determined by a functional relationship to exogenous predictors which we will call the f(x) component, a constant, and finally additional noise generated from either a Gaussian distribution with mean = 0, or Poisson distribution. The 4 series within a given sublist only differ based on the f(x) component of the series. One series, series.mvnormx, uses a matrix X generated by MASS::mvrnorm() with corresponding coefficients for the f(x) component. All other series use piece-wise functional relationships for the f(x) component of the series.

Usage

simulation_master_list

Format

A list containing 20 sublists each with 31 items:

series.len

the number of observations in the simulated time series

random.seed

the random seed used in set.seed() for all random components in the sublist.

arima.p

The AR order argument for stats::arima.sim().

arima.d

The differencing order argument for stats::arima.sim().

arima.q

The MA order argument for stats::arima.sim().

ar.coefficients

Coefficients for the AR process in stats::arima.sim(),NULL if arima.p=0.

ma.coefficients

Coefficients for the MA process in stats::arima.sim(),NULL if arima.q=0.

seasonal.periods

The number of periods in a full cycle for the periodic component of the series.

sin.coef

Coefficient on the sin term of the periodic component of the series.

cos.coef

Coefficient on the cos term of the periodic component of the series.

X.cols

Number of predictors used to generate the f(x) component of series.mvnormx.

X.mu

The mean vector used in MASS::mvrnorm() to generate X for the f(x) component of series.mvnormx.

X.Sigma

The covariance matrix generated by clusterGeneration::rcorrmatrix() used in MASS::mvrnorm() to generate X for the f(x) component of series.mvnormx.

X

The matrix of X.cols predictors generated by MASS::mvrnorm() used to generate the f(x) component of series.mvnormx.

x.coef

The vector of X.cols coefficients corresponding to the predictors of X used to generate the f(x) component of series.mvnormx.

x.chng.mu

The mean value used in stats::rnorm() used to generate x.chng.

x.chng.sd

The standard deviation value used in stats::rnorm() used to generate x.chng.

x.chng.coef1

A coefficient for x.chng used in all piece-wise functional relationship, f(x), components, as the coef argument to lin.to.sqrt() and quad.to.cubic and the coef1 argument to lin.coef.change .

x.chng.coef2

A coefficient for x.chng used in the piece-wise functional relationship, f(x), component of series.lin.coef.chng.x, as the coef2 argument to lin.coef.change .

x.chng.break.point

A value used in two piece-wise functional relationship, f(x), components, as the break.point argument to quad.to.cubic and lin.coef.change .

x.chng.break.point.sqrt

The max() of x.chng.break.point and some value > 0. Used in the piece-wise functional relationship, f(x), component of series.lin.to.sqrt.x , as the break.point argument to lin.to.sqrt.

x.chng

A vector of observations of a single predictor used to generate the f(x) component of all series other than series.mvnormx.

type.noise

The family of probability distributions to to generate the additional noise component.

poisson.rate

The lambda argument of stats::rpois() used to generate additional noise, only actually used if type.noise = 'poisson'.

norma.sd

The sd argument of stats::rnorm() used to generate additional noise, only actually used if type.noise = 'normal'.

constant

A numeric value which is the constant component of the series.

series.mvnormx

A simulated time series generated from the sum of ARIMA, Periodic, f(x), noise, and constant components. In this case f(x) represents linear relationships to the columns of the matrix X.

series.lin.to.sqrt.x

A simulated time series generated from the sum of ARIMA, Periodic, f(x), noise, and constant components. In this case f(x) represents a linear relationship to a single predictor x.chng which changes to a sqrt(x.chng) relationship when x.chng > x.chng.break.point.sqrt.

series.lin.coef.chng.x

A simulated time series generated from the sum of ARIMA, Periodic, f(x), noise, and constant components. In this case f(x) represents a linear relationship to a single predictor x.chng which changes coefficient when x.chng > x.chng.break.point.

series.quad.to.cubic.x

A simulated time series generated from the sum of ARIMA, Periodic, f(x), noise, and constant components. In this case f(x) represents a quadratic relationship to a single predictor x.chng which changes to a cubic relationship when x.chng > x.chng.break.point, in addition a coefficient changes sign at x.chng.break.point.

Details

Below we have the functional relationships used for the piece-wise series:

lin.to.sqrt <- function(x, break.point, coef){ if (x < break.point) { out <- coef * x } else { out <- sqrt(x) } return(out) }

quad.to.cubic <- function(x, break.point, coef){ if (x < break.point) { out <- coef * (x ** 2) } else { out <- -coef * (x ** 3) } return(out) }

lin.coef.change <- function(x, break.point, coef1, coef2){ if (x < break.point) { out <- coef1 * x } else { out <- coef2 * x } return(out) }

Source

https://github.com/mtrupiano1/knnwtsim/blob/main/data-raw/simulation_master_list.R