1. A method for predicting performance of a classifier trained using a sliding window into a training data set, the method comprising the steps of: sorting the training data set into a sorted training data set according to one or more first criteria based at least in part on temporal ordering; choosing one or more teaching subsets of approximately equal first predetermined size comprising first adjacent members of the sorted training data set and corresponding one or more testing subsets of approximately equal second predetermined size comprising at least one member from the sorted training data set that is temporally subsequent to all members of its corresponding one or more teaching subsets; training corresponding one or more classifiers using the one or more teaching subsets; classifying members of the corresponding one or more testing subsets using the corresponding one or more classifiers; comparing classifications assigned to members of the corresponding one or more testing subsets to corresponding true classifications assigned to corresponding members in the labeled training data to generate one or more performance estimates; and predicting performance of the classifier trained using with a sliding window into the training data of approximately the first predetermined size based on statistical analysis of the one or more performance estimates.