A model-agnostic framework to boost time-series forecasting by aligning internal representations with those of pretrained foundation models.
March 26, 2026
Original Paper
Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
arXiv · 2603.24262
The Takeaway
It introduces a practical way to distill the 'semantic wisdom' of massive, expensive time-series foundation models into lightweight, task-specific architectures. This allows practitioners to achieve high performance without the inference overhead of large-scale models.
From the abstract
Nowadays, time series forecasting is predominantly approached through the end-to-end training of deep learning architectures using error-based objectives. While this is effective at minimizing average loss, it encourages the encoder to discard informative yet extreme patterns. This results in smooth predictions and temporal representations that poorly capture salient dynamics. To address this issue, we propose ReGuider, a plug-in method that can be seamlessly integrated into any forecasting arch