I have a bunch of training examples (which are time series segments) that are used to train an algorithm. For any new example that is now presented to the algorithm for classification, i want to the determine how "similar" it is to the training examples (not to a specific one, but all of them) to check if it is "covered" by the training set.
Can you guys point me into some direction how to approach this? My first thought is that I have to come up with a problem specific similarity function, compare it with every training example and then use the mean of all comparisons.
One could also use one of the standard time series similarity measurements, but would those compare the examples with respect to their important features?