sarima.for {astsa} | R Documentation |
ARIMA forecasting.
sarima.for(xdata,n.ahead,p,d,q,P=0,D=0,Q=0,S=-1,tol=sqrt(.Machine$double.eps), no.constant=FALSE, plot=TRUE, plot.all=FALSE, xreg = NULL, newxreg = NULL, fixed=NULL, ...)
xdata |
univariate time series |
n.ahead |
forecast horizon (number of periods) |
p |
AR order |
d |
difference order |
q |
MA order |
P |
SAR order; use only for seasonal models |
D |
seasonal difference; use only for seasonal models |
Q |
SMA order; use only for seasonal models |
S |
seasonal period; use only for seasonal models |
tol |
controls the relative tolerance (reltol) used to assess convergence. The default is |
no.constant |
controls whether or not a constant is included in the model. If |
plot |
if TRUE (default) the data (or some of it) and the forecasts and bounds are plotted |
plot.all |
if TRUE, all the data are plotted in the graphic; otherwise, only the last 100 observations are plotted in the graphic. |
xreg |
Optionally, a vector or matrix of external regressors, which must have the same number of rows as the series. If this is used, |
newxreg |
New values of |
fixed |
optional numeric vector of the same length as the total number of parameters. If supplied, only parameters corresponding to NA entries will be estimated. |
... |
additional graphical arguments |
For example, sarima.for(x,5,1,0,1)
will forecast five time points ahead for an ARMA(1,1) fit to x. The output prints the forecasts and the standard errors of the forecasts, and supplies a graphic of the forecast with +/- 1 and 2 prediction error bounds.
pred |
the forecasts |
se |
the prediction (standard) errors |
You can find demonstrations of astsa capabilities at FUN WITH ASTSA.
The most recent version of the package can be found at https://github.com/nickpoison/astsa/.
In addition, the News and ChangeLog files are at https://github.com/nickpoison/astsa/blob/master/NEWS.md.
The webpages for the texts are https://www.stat.pitt.edu/stoffer/tsa4/ and https://www.stat.pitt.edu/stoffer/tsda/.
sarima.for(log(AirPassengers),12,0,1,1,0,1,1,12) # fun with the graphic sarima.for(log(AirPassengers),12,0,1,1,0,1,1,12, gg=TRUE, col=4, main='arf') # with regressors: nummy = length(soi) n.ahead = 24 nureg = time(soi)[nummy] + seq(1,n.ahead)/12 sarima.for(soi,n.ahead,2,0,0,2,0,0,12, xreg=time(soi), newxreg=nureg)