Description Usage Arguments Value Examples
Generates stationary or non-stationary time series data with mean or variance trend and different error distributions.
1 2 | ts.data.generator(TS, y0, delta, tau, phi, theta, error, omega, burnin,
continue)
|
TS |
Size of the time series |
y0 |
Y[0] value |
delta |
Drift constant |
tau |
Trend on the mean |
phi |
Matrix of autoregressive parameters over time |
theta |
Matrix of moving average parameters over time |
error |
Type of error and parameters Normal - c(ERROR_N, mean, stdv) Exponential - c(ERROR_E, mean, lambda) Triangle - c(ERROR_T, lower, upper, mode) |
omega |
Trend on the variance |
burnin |
Number of samples thrown away at the beginning of time series generation |
continue |
Continues a previous time series (Supports only AR(1) and NO MOVING AVERAGE) |
Time series of size N
1 2 | x <- 100
ts.data.generator(x, 0, 0, 0, 1, 0, c(ERROR_N,0,1), 0, 10, TRUE)
|
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