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.hodg...@gmail.com
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