Sets up the necessary backend for the SARIMA process.

`SARIMA(ar = 1, i = 0, ma = 1, sar = 1, si = 0, sma = 1, s = 12, 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.

- sar
A `vector`

or `integer`

containing either the coefficients for \(\Phi\)'s or the process number \(P\) for the Seasonal Autoregressive (SAR) term.

- si
An `integer`

containing the number of seasonal differences to be done.

- sma
A `vector`

or `integer`

containing either the coefficients for \(\Theta\)'s or the process number \(Q\) for the Seasonal Moving Average (SMA) term.

- s
An `integer`

containing the seasonality.

- sigma2
A `double`

value for the standard deviation, \(\sigma\), of the SARMA process.

## Value

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

- process.desc
\(AR*p\), \(MA*q\), \(SAR*P\), \(SMA*Q\)

- theta
\(\sigma\)

- plength
Number of parameters

- desc
Type of model

- desc.simple
Type of model (after simplification)

- print
String containing simplified model

- obj.desc
y desc replicated x times

- obj
Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1, i, si) )

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

## Details

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

## Examples

```
# Create an SARIMA(1,1,2)x(1,0,1) process
SARIMA(ar = 1, i = 1, ma = 2, sar = 1, si = 0, sma =1)
# Creates an SARMA(1,0,1)x(1,1,1) process with predefined coefficients.
SARIMA(ar=0.23, i = 0, ma=0.4, sar = .3, sma = .3)
```