auto.arima {forecast}R Documentation

Fit best ARIMA model to univariate time series

Description

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.

Usage

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, ic = c("aic","aicc", "bic"), 
           stepwise=TRUE, trace=FALSE, 
           approximation=length(x)>100 | frequency(x)>12, xreg=NULL)

Arguments

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

Details

Non-stepwise selection can be slow, especially for seasonal data. Non-seasonal differences chosen using kpss.test. Seasonal differences chosen using a variation on the Canova-Hansen test. Stepwise algorithm outlined in Hyndman and Khandakar (2008).

Value

Same as for arima

Author(s)

Rob J Hyndman

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

Arima

Examples

fit <- auto.arima(WWWusage)
plot(forecast(fit,h=20))

[Package forecast version 1.23 Index]