Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Uncertainty-Gated Generative Modeling

Xingrui Gu, Haixi Zhang

TL;DR

Uncertainty-Gated Generative Modeling is proposed, 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.

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).

Uncertainty-Gated Generative Modeling

TL;DR

Uncertainty-Gated Generative Modeling is proposed, 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.

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 0.1281), with improved robustness under shock intervals (mSE: 0.2739 0.1748).
Paper Structure (27 sections, 33 equations, 2 figures, 1 table)

This paper contains 27 sections, 33 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Uncertainty-Gated Generative Modeling
  4. Uncertainty decomposition.
  5. Uncertainty-Gated Principle (UG): uncertainty as control
  6. UG actions.
  7. Instantiation: probabilistic encoding, UG transformation, probabilistic generation
  8. Probabilistic encoder.
  9. Uncertainty-gated attention (relevance$\times$confidence routing; Eq. equation \ref{['eq:ug_action_route']}).
  10. Probabilistic decoder (predictive distribution and sharpness control).
  11. Training objective
  12. Risk-sensitive inference
  13. Experiment
  14. Conclusion
  15. Appendix
  16. ...and 12 more sections

Figures (2)

  • Figure 1: NYISO data characteristics.Top: Long-term day-ahead LMP trend (2018--2024) showing strong non-stationarity and extreme spikes. Bottom: Daily profile with hourly averages and 95% confidence intervals, highlighting pronounced intra-day heteroskedasticity.
  • Figure 2: Hourly probability density evolution of NYISO load/price. Distributions vary substantially across hours in location, scale, and tail mass, revealing strong intra-day heteroskedasticity and heavy-tailed extremes.