Description Usage Arguments Value Examples
Generates non-stationary time series data with mean trend and normal error distribution.
1 | ts.trend.mean(N, TS, delta, tau, phi, theta, error, seeds, burnin)
|
N |
Number of time series |
TS |
Size of the time series |
delta |
Mean |
tau |
Trend on the mean |
phi |
Vector of autoregressive parameters |
theta |
Vector of moving average parameters |
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) |
seeds |
Vector of seeds |
burnin |
Number of samples thrown away at the beginning of time series generation |
N time series of size TS
1 | ts.trend.mean(5, 5000, 0, 1, 0.9, 0, c(ERROR_N, 0, 1), c(645,983,653,873,432), 10)
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