`ARIMA.Rd`

Estimate an ARIMA model

ARIMA(formula, ic = c("aicc", "aic", "bic"), stepwise = TRUE, greedy = TRUE, approximation = FALSE, order_constraint = p + q + P + Q <= 5, ...)

formula | Model specification (see "Specials" section). |
---|---|

ic | The information criterion used in selecting the model. |

stepwise | Should stepwise be used? |

greedy | Should the stepwise search move to the next best option immediately? |

approximation | Should CSS be used during model selection? |

order_constraint | A logical predicate on the orders of |

... | Further arguments for arima |

`pdq`

special is used to specify non-seasonal components of the model.
pdq(p = 0:5, d = 0:2, q = 0:5, start.p = 2, start.q = 2)

`p` | |

The order of the non-seasonal auto-regressive (AR) terms. If multiple values are provided, the one which minimises | |

`ic` | will be chosen. |

`d` | |

The order of integration for non-seasonal differencing. If multiple values are provided, one of the values will be selected via repeated KPSS tests. | |

`q` | |

The order of the non-seasonal moving average (MA) terms. If multiple values are provided, the one which minimises | |

`ic` | will be chosen. |

`start.p` | |

If | |

`stepwise = TRUE` | , |

`start.p` | provides the initial value for |

`p` | for the stepwise search procedure. |

`start.q` | |

If | |

`stepwise = TRUE` | , |

`start.q` | provides the initial value for |

`q` | for the stepwise search procedure. |

`PDQ`

special is used to specify seasonal components of the model.
PDQ(P = 0:2, D = 0:1, Q = 0:2, period = NULL, start.P = 1, start.Q = 1)

`P` | |

The order of the seasonal auto-regressive (SAR) terms. If multiple values are provided, the one which minimises | |

`ic` | will be chosen. |

`D` | |

The order of integration for seasonal differencing. If multiple values are provided, one of the values will be selected via repeated heuristic tests (based on strength of seasonality from an STL decomposition). | |

`Q` | |

The order of the seasonal moving average (SMA) terms. If multiple values are provided, the one which minimises | |

`ic` | will be chosen. |

`period` | |

The periodic nature of the seasonality. This can be either a number indicating the number of observations in each seasonal period, or text to indicate the duration of the seasonal window (for example, annual seasonality would be "1 year"). | |

`start.P` | |

If | |

`stepwise = TRUE` | , |

`start.P` | provides the initial value for |

`P` | for the stepwise search procedure. |

`start.Q` | |

If | |

`stepwise = TRUE` | , |

`start.Q` | provides the initial value for |

`Q` | for the stepwise search procedure. |

`xreg()`

special. Common exogenous regressor specials as specified in `common_xregs`

can also be used. These regressors are handled using `stats::model.frame()`

, and so interactions and other functionality behaves similarly to `stats::lm()`

.
xreg(...)

`...` | |

Bare expressions for the exogenous regressors (such as | |

`log(x)` | ) |

# Manual ARIMA specification USAccDeaths %>% as_tsibble %>% model(arima = ARIMA(log(value) ~ pdq(0,1,1) + PDQ(0,1,1)))#> # A mable: 1 x 1 #> arima #> <model> #> 1 <ARIMA(0,1,1)(0,1,1)[12]>#> #>#>#> #>tsibbledata::global_economy %>% filter(Country == "Australia") %>% model(ARIMA(log(GDP) ~ Population))#> # A mable: 1 x 2 #> # Key: Country [1] #> Country `ARIMA(log(GDP) ~ Population)` #> <fct> <model> #> 1 Australia <LM w/ ARIMA(1,0,1) errors>