Mitigating Exposure Bias in Risk-Aware Time Series Forecasting with Soft Tokens

• URL: http://arxiv.org/abs/2512.10056v1
• Date: Wed, 10 Dec 2025 20:25:45 GMT
• Title: Mitigating Exposure Bias in Risk-Aware Time Series Forecasting with Soft Tokens
• Authors: Alireza Namazi, Amirreza Dolatpour Fathkouhi, Heman Shakeri,
• Abstract summary: We introduce Soft-Token Trajectory Forecasting (SoTra), which propagates continuous probability distributions to mitigate exposure bias and learn calibrated, uncertainty-aware trajectories.<n>In glucose forecasting, SoTra reduces average zone-based risk by 18%; in blood-pressure forecasting, it lowers effective clinical risk by approximately 15%.
• Score: 0.098314893665023
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autoregressive forecasting is central to predictive control in diabetes and hemodynamic management, where different operating zones carry different clinical risks. Standard models trained with teacher forcing suffer from exposure bias, yielding unstable multi-step forecasts for closed-loop use. We introduce Soft-Token Trajectory Forecasting (SoTra), which propagates continuous probability distributions (``soft tokens'') to mitigate exposure bias and learn calibrated, uncertainty-aware trajectories. A risk-aware decoding module then minimizes expected clinical harm. In glucose forecasting, SoTra reduces average zone-based risk by 18\%; in blood-pressure forecasting, it lowers effective clinical risk by approximately 15\%. These improvements support its use in safety-critical predictive control.

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