auto.arima: Fit best ARIMA model to univariate time series
In a1967fa/f-cast: Forecasting Functions for Time Series and Linear Models

Description Usage Arguments Details Value Author(s) References See Also Examples

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(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, ...)

`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`.

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).

Same as for Arima

Rob J Hyndman

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

Arima