auto.arima(x, d=NA, D=NA, max.p=5, max.q=5,
max.P=2, max.Q=2, max.order=5, start.p=2, start.q=2,
start.P=1, start.Q=1, stationary=FALSE, seasonal=TRUE,
ic=c("aicc","aic", "bic"), stepwise=TRUE, trace=FALSE,
approximation=(length(x)>100 | frequency(x)>12), xreg=NULL,
test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"),
allowdrift=TRUE, lambda=NULL, parallel=FALSE, num.cores=NULL)
TRUE
, restricts search to stationary models.FALSE
, restricts search to non-seasonal models.TRUE
, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.TRUE
, the list of ARIMA models considered will be reported.TRUE
, estimation is via conditional sums of squares andthe information criteria used for model selection are approximated. The final model is still computed using maximum likelihood estimation. Approximation should be used for long time sendiffs
for details.nsdiffs
for details.TRUE
, models with drift terms are considered.TRUE
and stepwise = FALSE
, then the specification search is done in parallel. This can give a significant speedup on mutlicore machines.parallel = TRUE
and stepwise = FALSE
. If NULL
, then the number of logical cores is automatically detected.arima
Arima
fit <- auto.arima(WWWusage)
plot(forecast(fit,h=20))
Run the code above in your browser using DataLab