arimaSpec: Automatic Modeling of a Scalar Non-Seasonal Time Series

In SLBDD: Statistical Learning for Big Dependent Data

Automatic Modeling of a Scalar Non-Seasonal Time Series

Description

Select an ARIMA model for a non-seasonal scalar time series. It uses augmented Dickey-Fuller (ADF) test to check for unit roots. The maximum degree of differencing is 2.

Usage

arimaSpec(
  zt,
  maxorder = c(5, 1, 4),
  criterion = "bic",
  output = FALSE,
  method = "CSS-ML",
  pv = 0.01
)

Arguments

zt	T by 1 vector of an observed scalar time series without missing values.
maxorder	Maximum order of (p,d,q) where p is the AR order, d the degree of differencing, and q the MA order. Default value is (5,1,4).
criterion	Information criterion used for model selection. Either AIC or BIC. Default is "bic".
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".
pv	P-value for unit-root test. Default is 0.01.

Details

Find the AR order by checking a pure AR model for the differenced series. The maximum AR order tried is min(default AR order and the order of pure AR model). Check the MA order by checking pure MA model using rank-based Ljung-Box statistics. The maximum MA order tried is the min(default MA order and the order of pure MA model). Finally, sequentially decreasing the AR order and increasing the MA order to obtain best models using the specified criterion function.

Value

A list containing:

• order - Regular ARIMA order.

• crit - Minimum criterion.

• include.mean - Switch about including mean in the model.

Examples

data(TaiwanAirBox032017)
fit <- arimaSpec(as.matrix(TaiwanAirBox032017[,1]))

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