ARIMA: Create an Autoregressive Integrated Moving Average (ARIMA)...
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Author(s) Examples

Sets up the necessary backend for the ARIMA process.

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

`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.

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

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)

JJB

# 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)