Hello,
To see the source code, type fitted.Arima (no parenthesis) at an R
prompt. It will show that they are computed as I've said.
So your second question is in order, where do the residuals come from?
To see the source code for arima() type the function name without the
parenthesis and it will show you that it calls functions written in C.
I don't have the time to look into it today but I'll put it on my to do
list.
Rui Barradas
Em 22-05-2013 16:41, Neuman Co escreveu:
Thanks, but this does not help me, because first of all, I do not know
how to look at the source code (just entering fitted() or
getAnywhere(fitted()) does not help,
second, your solution x-m$residuals does not be a solution, because
then the question is, where do the residuals come from?
2013/5/22 Rui Barradas <ruipbarra...@sapo.pt>:
Hello,
Since R is open source, you can look at the source code of package forecast
to know exactly how it is done. My guess would be
x - m$residuals
Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000
Hope this helps,
Rui Barradas
Em 22-05-2013 15:13, Neuman Co escreveu:
Hi,
3 down vote favorite
1
I am interested in forecasting a MA model.Therefore I have created a
very simple data set (three variables). I then adapted a MA(1) model
to it. The results are:
x<-c(2,5,3)
m<-arima(x,order=c(0,0,1))
Series: x
ARIMA(0,0,1) with non-zero mean
Coefficients:
ma1 intercept
-1.0000 3.5000
s.e. 0.8165 0.3163
sigma^2 estimated as 0.5: log likelihood=-3.91
AIC=13.82 AICc=-10.18 BIC=11.12
While the MA(1) model looks like this:
X_t=c+a_t+theta*a_{t-1}
and a_t is White Noise.
Now, I look at the fitted values:
library(forecast)
fitted(m)
Time Series:
Start = 1
End = 3
Frequency = 1
[1] 3.060660 4.387627 3.000000
I tried different ways, but I cant find out how the fitted values
(3.060660, 4.387627 and 3.000000) are calculated.
Any help would be very appreciated.
--
Neumann, Conrad
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