Dear R People:

I have the following situation.  I have observations that are 128 samples
per second, which is fine.  I want to fit them with ARIMA models, also fine.

My question is, please:  when I do my forecasting, do I need to do anything
special to the "n.ahead" parm, please?  Here is the initial setup:


> xx <- ts(rnorm(128),start=0,freq=128)
> str(xx)
 Time-Series [1:128] from 0 to 0.992: -1.07 0.498 1.508 0.354 -0.497 ...
> xx.ar <- arima(xx,order=c(1,0,0))
> str(xx.ar)
List of 13
 $ coef     : Named num [1:2] -0.0818 0.0662
  ..- attr(*, "names")= chr [1:2] "ar1" "intercept"
 $ sigma2   : num 1.06
 $ var.coef : num [1:2, 1:2] 7.78e-03 -5.09e-05 -5.09e-05 7.07e-03
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : chr [1:2] "ar1" "intercept"
  .. ..$ : chr [1:2] "ar1" "intercept"
 $ mask     : logi [1:2] TRUE TRUE
 $ loglik   : num -185
 $ aic      : num 376
 $ arma     : int [1:7] 1 0 0 0 128 0 0
 $ residuals: Time-Series [1:128] from 0 to 0.992: -1.133 0.338 1.477 0.406
-0.54 ...
 $ call     : language arima(x = xx, order = c(1, 0, 0))
 $ series   : chr "xx"
 $ code     : int 0
 $ n.cond   : int 0
 $ model    :List of 10
  ..$ phi  : num -0.0818
  ..$ theta: num(0)
  ..$ Delta: num(0)
  ..$ Z    : num 1
  ..$ a    : num 0.156
  ..$ P    : num [1, 1] 0
  ..$ T    : num [1, 1] -0.0818
  ..$ V    : num [1, 1] 1
  ..$ h    : num 0
  ..$ Pn   : num [1, 1] 1
 - attr(*, "class")= chr "Arima"
> predict(xx.ar,n.ahead=3)
$pred
Time Series:
Start = c(1, 1)
End = c(1, 3)
Frequency = 128
[1] 0.05346814 0.06728105 0.06615104

$se
Time Series:
Start = c(1, 1)
End = c(1, 3)
Frequency = 128
[1] 1.028302 1.031737 1.031760

>

Thanks for any help.

Sincerely,
Erin


-- 
Erin Hodgess
Associate Professor
Department of Computer and Mathematical Sciences
University of Houston - Downtown
mailto: erinm.hodgess@gmail.com

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