Holt Winters predict results differ from fitted data dramatically - r

Question

I am having a huge difference between my fitted data with HoltWinters and the predict data. I can understand there being a huge difference after several predictions but shouldn't the first prediction be the same number as the fitted data would be if it had one more number in the data set??
Please correct me if I'm wrong and why that wouldn't be the case?
Here is an example of the actual data.
1
1
1
2
1
1
-1
1
2
2
2
1
2
1
2
1
1
2
1
2
2
1
1
2
2
2
2
2
1
2
2
2
-1
1
Here is an example of the fitted data.
1.84401709401709
0.760477897417666
1.76593566042741
0.85435674207981
0.978449891674328
2.01079668445307
-0.709049507055536
1.39603638693742
2.42620183925688
2.42819282543689
2.40391946256294
1.29795840410863
2.39684770489517
1.35370435531208
2.38165200319969
1.34590347535205
1.38878761417551
2.36316132796798
1.2226736501825
2.2344269563083
2.24742853293732
1.12409156568888
Here is my R code.
randVal <- read.table("~/Documents/workspace/Roulette/play/randVal.txt", sep = "")
test<-ts(randVal$V1, start=c(1,133), freq=12)
test <- fitted(HoltWinters(test))
test.predict<-predict(HoltWinters(test), n.ahead=1*1)
Here is the predicted data after I expand it to n.ahead=1*12. Keep in mind that I only really want the first value. I don't understand why all the predict data is so low and close to 0 and -1 while the fitted data is far more accurate to the actual data..... Thank you.
0.16860570380268
-0.624454483845195
0.388808753990824
-0.614404235175936
0.285645402877705
-0.746997659036848
-0.736666618626855
0.174830187188718
-1.30499945596422
-0.320145850774167
-0.0917166719596059
-0.63970713627854

Answer 1

Sounds like you need a statistical consultation since the code is not throwing any errors. And you don't explain why you are dissatisfied with the results since the first value for those two calls is the same. With that in mind, you should realize that most time-series methods assume input from de-trended and de-meaned data, so they will return estimated parameters and values that would need in many cases to be offset to the global mean to predict on the original scale. (It's really bad practice to overwrite intermediate values as you are doing with 'test'.) Nonetheless, if you look at the test-object you see a column of yhat values that are on the scale of the input data.
Your question "I don't understand why all the predict data is so low and close to 0 and -1 while the fitted data is far more accurate to the actual data" doesn't say in what sense you think the "predict data[sic]" is "more accurate" than the actual data. The predict-results is some sort of estimate and they are split into components, as you would see if you ran the code on help page for predict:
plot(test,test.predict12)
It is not "data", either. It's not at all clear how it could be "more accurate" unless you has some sort of gold standard that you are not telling us about.