[R] want to forecast AR model & calculate R square (but which method is good)

[sagarnikam123]

i used "itsmr" package ,
want to forecast AR model & calculate R square to judge my
forecasting/prediction ,that is it good or not ?
i take original model(i)  &  make example/demo model(j) ( 20 numbers less
from original model)

#AR model  forecasting
i<-read.table(file.choose(),skip=1)   #taking times series data

used file:--->   http://r.789695.n4.nabble.com/file/n4572840/1AIS_B.txt
1AIS_B.txt 

i<-i$V1   #original data
> j<-i[1:(length(i)-20)]    #withdrawing last 20 numbers
> 
> dynamite_ori_20<-i[(length(i)-19):length(i)]  #original numbers(last 20)
> arar_j<-arar(y=j,h=20)    #AR forecasting

> #plotting to visualize
> k<-append(j,arar_j$pred)
> plotc(i,k)
> 
> 
> #calculates R squred for AR forcasted 
> #1st method
> x<-dynamite_ori_20    #original value
> y<-arar_j$pred    #predicted value
> f<-lm(y~x)
> 
> beta=f$coeff["x"]*(sd(x)/sd(y))
> r_square<-beta*cov(x,y)
> r_square
           x 
0.0008692213
______________________________________________________________________
#2) method 
> summary(f)[c("r.squared", "adj.r.squared")]
$r.squared
[1] 0.0173342

$adj.r.squared
[1] -0.03725834
______________________________________________________________________

which r_square i used to check my forecasting is good or bad?
is there any other method to check forecasting quality ?


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