Say I have some data for past 5 years and I have trained my classifier (anything decision tree, svm etc.) based on that i.e. given the appropriate input feature data and correct output labeling.
Now for current year when I have to make prediction (predicting the output) I can supply the input feature data I am having for the current year and the classifier would predict the correct output labels.
So far so good.
However suppose If I dont have the current input feature data, how can I go about making predictions just based on the past data?
For an example election prediction, i.e. which party would win from each constituency. In this we have lots of past data but no current input feature data so how to go about this?