# [R] Forecasting MA model different to manually computation?

Neuman Co neumancohu at gmail.com
Wed May 22 17:41:46 CEST 2013

```So I mean: How can I calculate them manually?

2013/5/22 Neuman Co <neumancohu at gmail.com>:
> 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?
>
>> 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,
>>
>>
>> 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.
>>>
>>>
>>>
>>> --
>>>
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>>>
>>
>
>
>
> --