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auto.arima {forecast}

Fit best ARIMA model to univariate time series
Package: 
forecast
Version: 
5.1

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, 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), xreg=NULL,
     test=c("kpss","adf","pp"), seasonal.test=c("ocsb","ch"),
     allowdrift=TRUE, lambda=NULL, parallel=FALSE, num.cores=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 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.
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.
seasonal.test
This determines which seasonal unit root test is used. See nsdiffs for details.
allowdrift
If TRUE, models with drift terms are considered.
lambda
Box-Cox transformation parameter. Ignored if NULL. Otherwise, data transformed before model is estimated.
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.

Details

Non-stepwise selection can be slow, especially for seasonal data. Stepwise algorithm outlined in Hyndman and Khandakar (2008) except that the default method for selecting seasonal differences is now the OCSB test rather than the Canova-Hansen test.

Values

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

Arima

Examples

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

Author(s)

Rob J Hyndman

Documentation reproduced from package forecast, version 5.1. License: GPL (>= 2)