Searches through the vector of lag orders to find the best AR model which
has lowest AIC, AICc or BIC value. It is implemented using OLS, and behaves
comparably to `stats::ar.ols()`

.

AR(formula, ic = c("aicc", "aic", "bic"), ...)

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

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

... | Further arguments for arima |

A model specification.

Exogenous regressors and `common_xregs`

can be specified in the model
formula.

The `order`

special is used to specify the lag order for the auto-regression.

order(p = 0:15, fixed = list())

`p` | The order of the auto-regressive (AR) terms. If multiple values are provided, the one which minimises `ic` will be chosen. |

`fixed` | A named list of fixed parameters for coefficients. The names identify the coefficient, beginning with `ar` , and then followed by the lag order. For example, `fixed = list(ar1 = 0.3, ar3 = 0)` . |

Exogenous regressors can be included in an ARIMA model without explicitly using the `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()`

.

The inclusion of a constant in the model follows the similar rules to `stats::lm()`

, where including `1`

will add a constant and `0`

or `-1`

will remove the constant. If left out, the inclusion of a constant will be determined by minimising `ic`

.

xreg(..., fixed = list())

`...` | Bare expressions for the exogenous regressors (such as `log(x)` ) |

`fixed` | A named list of fixed parameters for coefficients. The names identify the coefficient, and should match the name of the regressor. For example, `fixed = list(constant = 20)` . |

luteinizing_hormones <- as_tsibble(lh) fit <- luteinizing_hormones %>% model(AR(value ~ order(3))) report(fit)#> Series: value #> Model: AR(3) w/ mean #> #> Coefficients: #> constant ar1 ar2 ar3 #> 1.5375 0.6578 -0.0658 -0.2348 #> #> sigma^2 estimated as 0.1905 #> AIC = -14.48 AICc = -13.55 BIC = -7fit %>% forecast() %>% autoplot(luteinizing_hormones)