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.

James Balamuta

## 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)