Self-optimizing Teacher and Auto-matching Student Framework for Change-point Representation Learning in Time Series Forecasting

Real-world time series data is inherently complex, noisy, and exhibits abrupt changes, posing various challenges in data modeling. Given the ubiquity and importance of time-series data, accurately forecasting change points, instead of the overall predictive performance, has become increasingly attractive as it assists in risk mitigation and loss prevention. In this task, we argue that the past and future interactions involving the target points determine the comprehensive structure contributing to abrupt changes. However, traditional left-to-right auto-regressive approaches only consider the historical sequence, resulting in a flawed learning process and limited performance. In this paper, we extend the teacher-student learning and propose a novel S elf-optimizing T eacher and A uto-matching S tudent framework (named ST-AS) to predict change points in time series data. Our framework models change point representations specific to the target points by integrating future knowledge while avoiding data leakage. Specifically, we design a Gumbel-enhanced filter for our self-optimizing teacher, which constructs selected and filtered sub-groups to derive discriminative representations using a positive-unlabeled learning strategy. Given this well-trained teacher, we propose an adaptive pattern matcher for our auto-matching student model, which learns missing information by automatically aligning relevant features. After that, a novel two-stage dual-guided learning process is then designed to mimic teacher’s decision-making behavior and enhance student’s excavate capability. Finally, we conduct extensive experiments on four real-world datasets to demonstrate that our proposed ST-AS exhibits significantly better prediction performance compared to existing state-of-the-art alternatives.