Question

I am trying to use the following code for my autoregressive model parametere estimation:

 ar(file[,1], aic = TRUE, order.max = NULL,method = "mle")

Then, I have the results along with the following errors:

 Call:
 ar(x = file[, 1], aic = TRUE, order.max = NULL, method = "mle")

 Coefficients:
       1        2        3        4        5        6        7        8  
 -2.3811  -3.3336  -4.3599  -4.8660  -4.8251  -4.0216  -3.1113  -2.0082  
       9  
 -0.5511  

Order selected 9  sigma^2 estimated as  4.742e-11 
 Warning messages:
 1: In arima0(x, order = c(i, 0L, 0L), include.mean = demean) :
   possible convergence problem: optim gave code=1
 2: In arima0(x, order = c(i, 0L, 0L), include.mean = demean) :
   possible convergence problem: optim gave code=1
 3: In arima0(x, order = c(i, 0L, 0L), include.mean = demean) :
   possible convergence problem: optim gave code=1
 4: In arima0(x, order = c(i, 0L, 0L), include.mean = demean) :
   possible convergence problem: optim gave code=1

Is there a way to eliminate these errors in my autoregressive parameter estimation?

Actually, I am trying to do the forecasting based on this data using autoregressive model,

but I prefer first order autoregressive model, if possible.

However, even the forecasted values turned out to be far much irrelevant from the expected

forecasted values which is the problem..

Is there a way to do a good forecasting based on these data either from first autoregressive model

and/or any order autoregressive model?

I would greatly appreciate if you could provide any helps.

Thank you very much in advance!

Was it helpful?

Solution

Then just use:

model<-arima(file[,1],order=c(1,0,0))
predict(model,n.ahead=5)
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