SummaryModel: Collects All Models Specified by "arimaSpec"

In SLBDD: Statistical Learning for Big Dependent Data

Collects All Models Specified by "arimaSpec"

Description

Collects all models Specified by "arimaSpec".

Usage

SummaryModel(x, maxorder = c(5, 1, 3), criterion = "bic", method = "CSS")

Arguments

x	T by k data matrix: T data points in rows with each row being data at a given time point, and k time series in columns.
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 (5,1,3).
criterion	Information criterion used for model selection. Either AIC or BIC. Default is "bic".
method	Estimation method. See the arima command in R. Possible values are "CSS-ML", "ML", and "CSS". Default is "CSS".

Value

A list containing:

• Order - Orders (p, d, q) of each series. A matrix of (ncol(x),3). The three columns are "p", "d", "q".

• Mean - A logical vector indicating whether each series needs a constant (or mean).

• M1 - A matrix with three columns (p, 0, q). The number of rows is the number of stationary time series. M1 is NULL if there is no stationary series.

• M2 - A matrix with three columns (p, 1, q). The number of rows is the number of first-differenced series. M2 is NULL if there is no first-differenced series.

• M3 - A matrix with three columns (p, 2, q). The number of rows is the number of 2nd-differenced series. M3 is NULL if there is no 2nd-differenced series.

• data - Time series.

Examples

x <- matrix(rnorm(300, mean = 10, sd = 4), ncol = 3, nrow = 100)
summary <- SummaryModel(x)

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