ARIMA Forecasting
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. |
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 |
sarima.for(log(AirPassengers),12,0,1,1,0,1,1,12) # 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)
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