Uncertainty-Gated Generative Modeling

2026-03-08

AI Summary

This work proposes a novel approach, WIAE-GPF, that explicitly embeds uncertainty throughout the generative modeling pipeline for financial time series forecasting—a domain where overconfident models often yield high-risk mispredictions during market shocks. By integrating gated reparameterization with similarity- and confidence-based routing mechanisms, the method dynamically modulates representation learning and prediction generation. Furthermore, it incorporates uncertainty-driven regularization and calibration strategies to enhance reliability. Evaluated on the NYISO dataset, the model achieves a 63.5% reduction in mean squared error (MSE), improving from 0.3508 to 0.1281, and demonstrates markedly enhanced robustness during shock periods, with mSE decreasing from 0.2739 to 0.1748. These results indicate a significant mitigation of misprediction risk under extreme market events.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Scheduling under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Financial time-series forecasting is a high-stakes problem where regime shifts and shocks make point-accurate yet overconfident models dangerous. We propose Uncertainty-Gated Generative Modeling (UGGM), which treats uncertainty as an internal control signal that gates (i) representation via gated reparameterization, (ii) propagation via similarity and confidence routing, and (iii) generation via uncertainty-controlled predictive distributions, together with uncertainty-driven regularization and calibration to curb miscalibration. Instantiated on Weak Innovation AutoEncoder (WIAE-GPF), our UG-WIAE-GPF significantly improves risk-sensitive forecasting, delivering a 63.5\% MSE reduction on NYISO (0.3508 $\rightarrow$ 0.1281), with improved robustness under shock intervals (mSE: 0.2739 $\rightarrow$ 0.1748).

Problem

• financial time-series forecasting
• regime shifts
• uncertainty
• model overconfidence
• miscalibration

Innovation

• Uncertainty-Gated Generative Modeling
• risk-sensitive forecasting
• uncertainty calibration
• gated reparameterization
• confidence routing