ARIMA: Create an Autoregressive Integrated Moving Average (ARIMA)...

In schoi355/gmwm: Generalized Method of Wavelet Moments

Create an Autoregressive Integrated Moving Average (ARIMA) Process

Description

Sets up the necessary backend for the ARIMA process.

Usage

ARIMA(ar = 1, i = 0, ma = 1, sigma2 = 1)

Arguments

ar	A vector or integer containing either the coefficients for phi's or the process number p for the Autoregressive (AR) term.
i	An integer containing the number of differences to be done.
ma	A vector or integer containing either the coefficients for theta's or the process number q for the Moving Average (MA) term.
sigma2	A double value for the standard deviation, sigma, of the ARIMA process.

Details

A variance is required since the model generation statements utilize randomization functions expecting a variance instead of a standard deviation like R.

Value

An S3 object with called ts.model with the following structure:

process.desc

AR x p, MA x q

theta

sigma

plength

Number of Parameters

obj.desc

y desc replicated x times

obj

Depth of Parameters e.g. list(c(length(ar),length(ma),1) )

starting

Guess Starting values? TRUE or FALSE (e.g. specified value)

Author(s)

JJB

Examples

# Create an ARMA(1,2) process
ARIMA(ar=1,2)
# Creates an ARMA(3,2) process with predefined coefficients.
ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3))

# Creates an ARMA(3,2) process with predefined coefficients and standard deviation
ARIMA(ar=c(0.23,.43, .59), ma=c(0.4,.3), sigma2 = 1.5)

schoi355/gmwm documentation built on April 11, 2022, 1:21 a.m.