PHaSe: Adaptive Time Series Data Selection via Prototype Hardness

Real-world time series data vary in quality, yet conventional training typically grants equal training opportunities to all instances. Although data selection can prioritize useful instances, assessing instances individually does not explicitly account for the overall learning state of their shared temporal patterns. In this work, we propose Prototype HArdness-guided SElection (PHaSe), an adaptive time series data selection framework that aligns training allocation with the model's evolving learning needs across temporal patterns. PHaSe organizes instances into representative pattern groups and estimates their relative learning needs, termed hardness, from recent loss trajectories, providing a pattern-level basis for identifying instances that require further learning. It then transfers pattern-level hardness to instance priorities, modulates these priorities by assignment concentration, and converts them into sampling probabilities. To adapt pattern organization to changing training priorities, we refit prototypes on selected instances and combine the fitted centers with historical centers through normalized momentum updates. Compared with the evaluated baselines in individual settings, PHaSe achieves up to 16.4% lower long-term MSE, 4.8% lower short-term sMAPE, and 74.2% higher classification accuracy, while using only half of the training instances per epoch.