# [R] Seasonal ARIMA question - stat package (formerly ts)

From: Keith Campbell (keithc@rotellacapital.com)
Date: Tue 25 May 2004 - 07:15:16 EST

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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
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

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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