To whom it may concern:
 
I am trying to better understand the functionality of 'R' when making
arima predictions to avoid any "Black Box" disadvantages.
 
I'm fitting a seasonal arima model using the following command (having
already loaded 'stat' package).
arimaSeason <-
arima(Data,order=c(1,0,1),seasonal=list(order=c(1,0,1),period=12))
 
I can then generate subsequent predictions using the 'predict' function.
However, I can't seem to duplicate these predictions in a separate
program using the model coefficients.  From duplicating simpler models,
I understand the input variables are adjusted by the intercept term.
(e.g. for an arima(1,0,0) the prediction equation is   y(t) = beta1 * (
y(t-1) - beta0 )  +  beta0 ....where beta0 is the intercept)
 
Currently, I've expected the prediction to follow the equation below:
y(t)  =  beta0  +  [beta1*( y(t-1) - beta0 )]  +  [beta2 * epsilon(t-1)]
+  [beta3 * (y(t-12) - beta0)]  +  [beta4 * epsilon(t-12)]
 
This has proved unsuccessful.  What equation underlies this arima
prediction?  Is there something different that happens
 
Many thanks for your help,
Keith Campbell
 
 
Keith Campbell 
Researcher
ROTELLA CAPITAL MANAGEMENT |180 N. Stetson Suite 5100 I Chicago, IL
60601 |  t: 312.706.0442 |  f: 312.861.0468  | 
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