sarimaSpec function

Automatic Modeling of a Scalar Seasonal Time Series

Auto-model specification of a scalar seasonal time series. The period should be given.

sarimaSpec(
  zt,
  maxorder = c(2, 1, 3),
  maxsea = c(1, 1, 1),
  criterion = "bic",
  period = 12,
  output = FALSE,
  method = "CSS-ML",
  include.mean = TRUE
)

Arguments

• zt: T by 1 vector of an observed scalar time series without missing values.
• maxorder: Maximum order of $(p,d,q)$. $p$ is the AR order, $d$ the degree of differencing, and $q$ The MA order. Default value is (2,1,3).
• maxsea: Maximum order of $(P,D,Q)$. $P$ is the seasonal AR order, $D$ the degree of seasonal differencing, and $Q$ the seasonal MA order. Default value is (1,1,1).
• criterion: Information criterion used for model selection. Either AIC or BIC. Default is "bic".
• period: Seasonal period. The default is 12.
• output: If TRUE it returns the differencing order, the selected order and the minimum value of the criterion. Default is TRUE.
• method: Estimation method. See the arima command in R. Possible values are "CSS-ML", "ML", and "CSS". Default is "CSS-ML".
• include.mean: Should the model include a mean/intercept term? Default is TRUE.

Returns

A list containing:

• data - The time series. If any transformation is taken, "data" is the transformed series.
• order - Regular ARIMA order.
• sorder - Seasonal ARIMA order.
• period - Seasonal period.
• include.mean - Switch about including mean in the model.

Details

ADF unit-root test is used to assess seasonal and regular differencing. For seasonal unit-root test, critical value associated with pv = 0.01 is used.

Examples

data(TaiwanAirBox032017)
output <- sarimaSpec(TaiwanAirBox032017[1:100,1])