I'm missing something when it comes to predicting the label for the future

So every so often I pick up rapidminer do a few tutorials and then try to do something on my own only to get stuck and put it away for a few months before getting ambitious again. Anyway its the same problem always. Let's say I have 7 weeks of data with 4 columns (A,B,C,X) where X is the label. I create a model, let's say using a decision tree, where I split the data sending first 6 weeks for training and the 7th week for testing. I apply the model and like what I see where out of all the data the model predicts week 7 results with about %93 accuracy. Great! Here is where I'm confused every time. Using this info how can I predict what week 8 will be? If you go back to the columns I have A,B,C the only column in my file that I know will be column A. I won't know B nor C values until that week comes which of course helps predict X. I was under the impression that rapidminer would use the information that I can supply and maybe use some type of average or medium of the data from prior weeks to fill in any gaps but I assume I'm way off because if I pass all 0's in B and C I'm getting no results. So can someone please help understand what I'm missing because I'm sure it's an ubderstanding of predictive modeling which will open my eyes to what's going on? Should be creating a week 8 file with averages from weeks 1 through 7 or something and pass that in like test data and use weeks 1 thru 7 as training data?

Thanks in advance