Described is an improved approach to implement an offline learning approach for machine learning that employs a window-based technique for predicting values within the window, and where outliers are identified and discarded from consideration. This approach efficiently permits offline learning to be employed in a manner that minimizes false positives, while also improving the quality of the data should retaining be required.