Description Usage Arguments Details Value Examples

Automatic selection and estimation of a regular or possibly seasonal ARIMA model for a given time series.

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`zt` |
T by 1 vector of an observed scalar time series without any missing values. |

`maxorder` |
Maximum order of |

`criterion` |
Information criterion used for model selection. Either AIC or BIC. Default is "bic". |

`period` |
Seasonal period. Default value is 12. |

`output` |
If TRUE it returns the differencing order, the selected order and the minimum value of the criterion. Default is TRUE. |

`method` |
Estimation method. See the arima command in R. Possible values are "CSS-ML", "ML", and "CSS". Default is "CSS-ML". |

`pv` |
P-value for unit-root test. Default value is 0.01. |

`spv` |
P-value for detecting seasonality. Default value is 0.01. |

`transpv` |
P-value for checking non-linear transformation. Default value is 0.05. |

`nblock` |
Number of blocks used in checking non-linear transformations. Default value is floor(sqrt(T)). |

The program follows the following steps:

Check for seasonality: fitting a multiplicative ARIMA(p,0,0)(1,0,0)_s model to a scalar time series and testing if the estimated seasonal AR coefficient is significant.

Check for non-linear transformation: the series is divided into a given number of consecutive blocks and in each of them the Mean Absolute Deviation (MAD) and the median is computed. A regression of the log of the MAD with respect to the log of the median is run and the slope defines the non-linear transformation.

Select orders: maximum order of

*(p,d,q)*.

A list containing:

data - The time series. If any non-linear transformation is taken, "data" is the transformed series.

order - Regular ARIMA order.

sorder - Seasonal ARIMA order.

period - Seasonal period.

include.mean - Switch concerning the inclusion of mean in the model.

1 2 | ```
data(TaiwanAirBox032017)
fit <- arimaID(TaiwanAirBox032017[,1])
``` |

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