Sets up the necessary backend for the SARMA process.

SARMA(ar = 1, ma = 1, sar = 1, 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.

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.

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

A integer indicating the seasonal value of the data.

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

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) )

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.

James Balamuta

## Examples

# Create an SARMA(1,2)x(1,1) process
SARMA(ar = 1, ma = 2,sar = 1, sma =1)

# Creates an SARMA(1,1)x(1,1) process with predefined coefficients.
SARMA(ar=0.23, ma=0.4, sar = .3, sma = .3)