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
AR(formula, ic = c("aicc", "aic", "bic"), ...)
Model specification (see "Specials" section).
The information criterion used in selecting the model.
Further arguments for arima
Exogenous regressors and
common_xregs can be specified in the model
A model specification.
order special is used to specify the lag order for the auto-regression.
order(p = 0:15, fixed = list())
|| The order of the auto-regressive (AR) terms. If multiple values are provided, the one which minimises
|| A named list of fixed parameters for coefficients. The names identify the coefficient, beginning with
Exogenous regressors can be included in an AR 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
The inclusion of a constant in the model follows the similar rules to
stats::lm(), where including
1 will add a constant and
-1 will remove the constant. If left out, the inclusion of a constant will be determined by minimising
xreg(..., fixed = list())
|| Bare expressions for the exogenous regressors (such as
|| A named list of fixed parameters for coefficients. The names identify the coefficient, and should match the name of the regressor. For example,
Forecasting: Principles and Practices, Vector autoregressions (section 11.2)
luteinizing_hormones <- as_tsibble(lh) fit <- luteinizing_hormones %>% model(AR(value ~ order(3))) report(fit) fit %>% forecast() %>% autoplot(luteinizing_hormones)
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