auto.arima(y, d=NA, D=NA, max.p=5, max.q=5, max.P=2, max.Q=2, max.order=5, max.d=2, max.D=1, 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), truncate=NULL, xreg=NULL, test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"), allowdrift=TRUE, allowmean=TRUE, lambda=NULL, biasadj=FALSE, parallel=FALSE, num.cores=2, x=y, ...)
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 series or a high seasonal period to avoid excessive computation times.
truncatevalues of the series are used to select a model when
approximation=TRUE. All observations are used if either
TRUE, models with drift terms are considered.
TRUE, models with a non-zero mean are considered.
stepwise = FALSE, then the specification search is done in parallel. This can give a significant speedup on mutlicore machines.
parallel = TRUEand
stepwise = FALSE. If
NULL, then the number of logical cores is automatically detected and all available cores are used.