[R] Forecasting MA model different to manually computation?

[Neuman Co]

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 <ruipbarradas at 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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>> and provide commented, minimal, self-contained, reproducible code.
>>
>



-- 
Neumann, Conrad