# auto.arima

##### Fit best ARIMA model to univariate time series

Returns best ARIMA model according to either AIC, AICc or BIC value. The function conducts a search over possible model within the order constraints provided.

- Keywords
- ts

##### Usage

```
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) > 150 | 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, ...)
```

##### Arguments

- 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 and the 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`

.

##### Details

The default arguments are designed for rapid estimation of models for many time series.
If you are analysing just one time series, and can afford to take some more time, it
is recommended that you set `stepwise=FALSE`

and `approximation=FALSE`

.

The number of seasonal differences is sometimes poorly chosen. If your data shows strong
seasonality, try setting `D=1`

rather than relying on the automatic selection of `D`

.

Non-stepwise selection can be slow, especially for seasonal data. The stepwise algorithm outlined in Hyndman and Khandakar (2008) is used except that the default method for selecting seasonal differences is now the OCSB test rather than the Canova-Hansen test. There are also some other minor variations to the algorithm described in Hyndman and Khandakar (2008).

##### Value

Same as for `Arima`

##### References

Hyndman, R.J. and Khandakar, Y. (2008) "Automatic time series
forecasting: The forecast package for R", *Journal of Statistical
Software*, **26**(3).

##### See Also

##### Examples

```
# NOT RUN {
fit <- auto.arima(WWWusage)
plot(forecast(fit,h=20))
# }
```

*Documentation reproduced from package forecast, version 8.2, License: GPL-3*