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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