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.truncate values of the series are used to select a model when truncate is not NULL and approximation=TRUE. All observations are used if either truncate=NULL or approximation=FALSE.y.ndiffs for details.nsdiffs for details.TRUE, models with drift terms are considered.TRUE, models with a non-zero mean 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 and all available cores are used.arima.Arimaplot(forecast(fit,h=20))
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