Most programs are repetitive, where similar behavior can be seen at different execution times. Proposed on-line systems automatically group these similar intervals of execution into phases, where the intervals in a phase have homogeneous behavior and similar resource requirements. These systems are driven by algorithms that dynamically classify intervals of execution into phases and predict phase changes. In this paper, we examine several improvements to dynamic phase classification and prediction. The first improvement is to appropriately deal with phase transitions. This modification identifies phase transitions for what they are, instead of classifying them into a new phase, which increases phase prediction accuracy. We also describe an adaptive system that dynamically adjusts classification thresholds and splits phases with poor homogeneity. This modification increase the homogeneity of the hardware metrics across the intervals in each phase. We improve phase prediction accuracy by applying confidence to phase prediction, and we develop architectures that can accurately predict the outcome of the next phase change, and the length of the next phase.