Searches through the model space specified in the specials to identify the
best ARIMA model which has lowest AIC, AICc or BIC value. It is implemented
stats::arima() and allows ARIMA models to be used in the fable
ARIMA(formula, ic = c("aicc", "aic", "bic"), stepwise = TRUE, greedy = TRUE, approximation = NULL, order_constraint = p + q + P + Q <= 6, unitroot_spec = unitroot_options(), ...)
Model specification (see "Specials" section).
The information criterion used in selecting the model.
Should stepwise be used?
Should the stepwise search move to the next best option immediately?
Should CSS (conditional sum of squares) be used during model selection? The default (
A logical predicate on the orders of
A specification of unit root tests to use in the
Further arguments for
A model specification.
pdq special is used to specify non-seasonal components of the model.
pdq(p = 0:5, d = 0:2, q = 0:5, p_init = 2, q_init = 2)
|The order of the non-seasonal auto-regressive (AR) terms. If multiple values are provided, the one which minimises |
|The order of integration for non-seasonal differencing. If multiple values are provided, one of the values will be selected via repeated KPSS tests.|
|The order of the non-seasonal moving average (MA) terms. If multiple values are provided, the one which minimises |
PDQ special is used to specify seasonal components of the model.
PDQ(P = 0:2, D = 0:1, Q = 0:2, period = NULL, P_init = 1, Q_init = 1)
|The order of the seasonal auto-regressive (SAR) terms. If multiple values are provided, the one which minimises |
|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).|
|The order of the seasonal moving average (SMA) terms. If multiple values are provided, the one which minimises |
|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").|
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
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
|Bare expressions for the exogenous regressors (such as |
# 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)>#> #>#>#> #>#>#> #>#>#> #>tsibbledata::global_economy %>% filter(Country == "Australia") %>% model(ARIMA(log(GDP) ~ Population))#> Warning: NaNs produced#> # A mable: 1 x 2 #> # Key: Country  #> Country `ARIMA(log(GDP) ~ Population)` #> <fct> <model> #> 1 Australia <LM w/ ARIMA(2,0,0) errors>