I have some fitted ARIMA model:
> fit Series: mydata ARIMA(1,0,1) with non-zero mean Box Cox transformation: lambda= 0.1088793 Coefficients: ar1 ma1 mean 0.4536 -0.1702 -0.1868 s.e. 0.0094 0.0104 0.0033 sigma^2 estimated as 0.461: log likelihood=-98549.78 AIC=197107.5 AICc=197107.5 BIC=197145.4
Now I want to check the prediction of this model. To do so, I want just to elongate some primer sequence
Thus, I use
fcast <- forecast(object = primer, model = fit)
My problems are:
1) I need only one value per step, but not mean
2) I am not sure, that this syntactic construction does what I need: use prior information only from
primer and produces
lambda adjusted results
I found, that I should use
simulate function, and it will generate the sample paths conditional on the previous data.
Thus I still have a question how to replace the data used for fitting by primer data?