`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, ...)`

y

a univariate time series

d

Order of first-differencing. If missing, will choose a value based on KPSS test.

D

Order of seasonal-differencing. If missing, will choose a value based on OCSB test.

max.p

Maximum value of p

max.q

Maximum value of q

max.P

Maximum value of P

max.Q

Maximum value of Q

max.order

Maximum value of p+q+P+Q if model selection is not stepwise.

max.d

Maximum number of non-seasonal differences

max.D

Maximum number of seasonal differences

start.p

Starting value of p in stepwise procedure.

start.q

Starting value of q in stepwise procedure.

start.P

Starting value of P in stepwise procedure.

start.Q

Starting value of Q in stepwise procedure.

stationary

If

`TRUE`

, restricts search to stationary models.seasonal

If

`FALSE`

, restricts search to non-seasonal models.ic

Information criterion to be used in model selection.

stepwise

If

`TRUE`

, will do stepwise selection (faster). Otherwise, it searches over all models. Non-stepwise selection can be very slow, especially for seasonal models.trace

If

`TRUE`

, the list of ARIMA models considered will be reported.approximation

If

`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

An integer value indicating how many observations to use in model selection. The last

`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`

.xreg

Optionally, a vector or matrix of external regressors, which must have the same number of rows as

`y`

.test

Type of unit root test to use. See

`ndiffs`

for details.seasonal.test

This determines which seasonal unit root test is used. See

`nsdiffs`

for details.allowdrift

If

`TRUE`

, models with drift terms are considered.allowmean

If

`TRUE`

, models with a non-zero mean are considered.lambda

Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated.

biasadj

Use adjusted back-transformed mean for Box-Cox transformations. If TRUE, point forecasts and fitted values are mean forecast. Otherwise, these points can be considered the median of the forecast densities.

parallel

If

`TRUE`

and `stepwise = FALSE`

, then the specification search is done in parallel. This can give a significant speedup on mutlicore machines.num.cores

Allows the user to specify the amount of parallel processes to be used if

`parallel = TRUE`

and `stepwise = FALSE`

. If `NULL`

, then the number of logical cores is automatically detected and all available cores are used.x

Deprecated. Included for backwards compatibility.

...

Additional arguments to be passed to

`arima`

.`Arima`

```
plot(forecast(fit,h=20))
```

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