sarimaSpec: Automatic Modeling of a Scalar Seasonal Time Series

In SLBDD: Statistical Learning for Big Dependent Data

Automatic Modeling of a Scalar Seasonal Time Series

Description

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

Usage

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.

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.

Value

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.

Examples

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

SLBDD documentation built on April 27, 2022, 5:08 p.m.