sim_from_fitted {arfima}  R Documentation 
This function simulates an long memory ARIMA time series, with one of fractionally differenced white noise (FDWN), fractional Gaussian noise (FGN), powerlaw autocovariance (PLA) noise, or short memory noise and possibly seasonal effects.
sim_from_fitted(n, model, X = NULL, seed = NULL)
n 
The number of points to be generated. 
model 
The model to be simulated from. The phi and theta arguments
should be vectors with the values of the AR and MA parameters. Note that
BoxJenkins notation is used for the MA parameters: see the "Details"
section of 
X 
The xreg matrix to add to the series, required if there is an xreg
argument in 
seed 
An optional seed that will be set before the simulation. If

A suitably defined stationary series is generated, and if either of the
dints (nonseasonal or seasonal) are greater than zero, the series is
integrated (inversedifferenced) with zinit equalling a suitable amount of
0s if not supplied. Then a suitable amount of points are taken out of the
beginning of the series (i.e. dint + period * seasonal dint = the length of
zinit) to obtain a series of length n. The stationary series is generated
by calculating the theoretical autovariance function and using it, along
with the innovations to generate a series as in McLeod et. al. (2007).
Note: if you would like to fit from parameters, use the funtion,
arfima.sim
.
A sample (or list of samples) from a multivariate normal distribution that has a covariance structure defined by the autocovariances generated for given parameters. The sample acts like a time series with the given parameters. The returned value will be a list if the fit is multimodal.
JQ (Justin) Veenstra
McLeod, A. I., Yu, H. and Krougly, Z. L. (2007) Algorithms for Linear Time Series Analysis: With R Package Journal of Statistical Software, Vol. 23, Issue 5
Veenstra, J.Q. Persistence and Antipersistence: Theory and Software (PhD Thesis)
P. Borwein (1995) An efficient algorithm for Riemann Zeta function Canadian Math. Soc. Conf. Proc., 27, pp. 2934.
set.seed(6533) sim < arfima.sim(1000, model = list(phi = .2, dfrac = .3, dint = 2)) fit < arfima(sim, order = c(1, 2, 0)) fit sim2 < sim_from_fitted(100, fit) fit2 < arfima(sim2, order = c(1, 2, 0)) fit2 set.seed(2266) #Fairly pathological series to fit for this package series = arfima.sim(500, model=list(phi = 0.98, dfrac = 0.46)) X = matrix(rnorm(1000), ncol = 2) colnames(X) < c('c1', 'c2') series_added < series + X%*%c(2, 5) fit < arfima(series, order = c(1, 0, 0), numeach = c(2, 2)) fit_X < arfima(series_added, order=c(1, 0, 0), xreg=X, numeach = c(2, 2)) from_series < sim_from_fitted(1000, fit) fit1a < arfima(from_series[[1]], order = c(1, 0, 0), numeach = c(2, 2)) fit1a fit1 < arfima(from_series[[1]], order = c(1, 0, 0)) fit1 fit2 < arfima(from_series[[1]], order = c(1, 0, 0)) fit2 fit3 < arfima(from_series[[1]], order = c(1, 0, 0)) fit3 fit4 < arfima(from_series[[1]], order = c(1, 0, 0)) fit4 Xnew = matrix(rnorm(2000), ncol = 2) from_series_X < sim_from_fitted(1000, fit_X, X=Xnew) fit_X1a < arfima(from_series_X[[1]], order=c(1, 0, 0), xreg=Xnew, numeach = c(2, 2)) fit_X1a fit_X1 < arfima(from_series_X[[1]], order=c(1, 0, 0), xreg=Xnew) fit_X1 fit_X2 < arfima(from_series_X[[2]], order=c(1, 0, 0), xreg=Xnew) fit_X2 fit_X3 < arfima(from_series_X[[3]], order=c(1, 0, 0), xreg=Xnew) fit_X3 fit_X4 < arfima(from_series_X[[4]], order=c(1, 0, 0), xreg=Xnew) fit_X4