best.arima {forecast} | R Documentation |
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.
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)
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. |
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 (2007).
Same as for arima
Rob J Hyndman
Hyndman, R.J. and Khandakar, Y. (2007) "Automatic time series forecasting: The forecast package for R", Journal of Statistical Software, to appear.
fit <- auto.arima(WWWusage) plot(forecast(fit,h=20))