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 |

seasonal | If |

ic | Information criterion to be used in model selection. |

stepwise | If |

trace | If |

approximation | If |

truncate | An integer value indicating how many observations to use in
model selection. The last |

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

test | Type of unit root test to use. See |

seasonal.test | This determines which seasonal unit root test is used.
See |

allowdrift | If |

allowmean | If |

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 |

num.cores | Allows the user to specify the amount of parallel processes
to be used if |

x | Deprecated. Included for backwards compatibility. |

... | Additional arguments to be passed to |

Same as for `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).

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