Estimate an ARIMA model

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

Arguments

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 p, d, q, P, D and Q to consider in the search.

...

Further arguments for arima

Specials

pdq

The 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
icwill 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
icwill be chosen.
start.p
If
stepwise = TRUE,
start.pprovides the initial value for
pfor the stepwise search procedure.
start.q
If
stepwise = TRUE,
start.qprovides the initial value for
qfor the stepwise search procedure.

PDQ

The 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
icwill 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
icwill 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.Pprovides the initial value for
Pfor the stepwise search procedure.
start.Q
If
stepwise = TRUE,
start.Qprovides the initial value for
Qfor the stepwise search procedure.

xreg

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

Examples

# 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]>
# Automatic ARIMA specification library(tsibble)
#> #> Attaching package: ‘tsibble’
#> The following objects are masked from ‘package:stats’: #> #> filter, lag
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>