ts.trend.autocorrelation: Time Series Data Generator with Autocorrelation Trend

In gnardin/stationarity: Time Series Stationarity Test

Description

Generates non-stationary time series data with autocorrelation trend and normal error distribution.

Usage

ts.trend.autocorrelation(N, TS, delta, phis, thetas, error, seeds, burnin)

Arguments

N	Number of time series
TS	Size of the time series
delta	Mean
phis	Vector with the minimum and maximum autoregressive values
thetas	Vector with the minimum and maximum moving average values
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

Value

N time series of size TS

Examples

ts.trend.autocorrelation(5, 5000, 0, c(-0.9, 0.9), c(0, 0), c(ERROR_N, 0, 1),
c(645,983,653,873,432), 10)

gnardin/stationarity documentation built on May 17, 2019, 7:29 a.m.