auto.arima

0th

Percentile

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(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,
           test=c("kpss","adf"), allowdrift=TRUE)
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
xreg
Optionally, a vector or matrix of external regressors, which must have the same number of rows as x.
test
Type of unit root test to use. See ndiffs for details.
allowdrift
If TRUE, models with drift terms are considered.
Details

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

Value

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

Aliases
  • best.arima
  • auto.arima
Examples
fit <- auto.arima(WWWusage)
plot(forecast(fit,h=20))
Documentation reproduced from package forecast, version 2.05, License: GPL (>= 2)

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