Uncertainty-Gated Generative Modeling

Xingrui Gu; Haixi Zhang

cs.LGMar 8, 2026

Uncertainty-Gated Generative Modeling

Authors: Xingrui Gu, Haixi Zhang

Organizations: Electrical Engineering and Computer Science, University of California, Berkeley, Berkeley, CA, USA · School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

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).

Figures & tables

	Pointwise		Normalized		Robust (Med.)		Scale-free
Model	MSE $\downarrow$	MAE $\downarrow$	NMSE $\downarrow$	NMAE $\downarrow$	mSE $\downarrow$	mAE $\downarrow$	MAPE $\downarrow$	MASE $\downarrow$
Proposed method (WIAE-based)
UG-WIAE-GPF (Ours)	0.1281	0.2550	0.0119	0.0280	0.1748	0.2145	0.2515	0.0220
WIAE-GPF (2024)	0.3508	0.3835	0.0220	0.0399	0.2739	0.3466	0.3958	0.3990
Transformer-/decomposition-based baselines
PatchTST (2022)	1.7508	1.0866	0.0387	0.0320	10.5493	3.2480	3.3637	0.1648

Table 1: Comparison with state-of-the-art forecasters on NYISO (2018–2024). Lower is better. Best is bold; second best is underlined (ties share the same rank).

Appendix figures & tables2 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 1 — 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.

Time Series Forecasting

Time series forecasting predicts future values from ordered observations using statistical, neural, and probabilistic models to support decisions under uncertainty.

See all 858 papers

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting
Mar 25, 2026Yijun Wang, Qiyuan Zhuang, Larysa Marchanka +1Probabilistic Time Series Forecasting Uncertainty Quantification
Department of Computer Science, Southeast University, Nanjing, China · Francisk Skorina Gomel State University, Belarus
Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series
Jul 25, 2026Ziling Ma, Junshu Jiang, Ángel López-Oriona +2Multivariate Time Series Forecasting Conformal Prediction
Statistics Department, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia · NeuroAI Laboratory, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia
SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models
Sep 23, 2026Xin-Yu Hu, Shuang Liang, Cheng Feng +1Uncertainty Quantification Time Series Forecasting
National Key Laboratory for Novel Software Technology, Nanjing University, China. · School of Intelligent Science and Technology, Nanjing University, China. · Siemens Data and AI Research, Beijing, China. +1

Mar 25, 2026J/K move · Enter open · S save

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting

Yijun Wang, Qiyuan Zhuang, Larysa Marchanka, Xiu-Shen Wei

Department of Computer Science, Southeast University, Nanjing, China · Francisk Skorina Gomel State University, Belarus

Probabilistic Time Series Forecasting Uncertainty Quantification Uncertainty Calibration Variational Autoencoders

Open paper AskPDF arXiv

Real-world time series exhibit temporally structured uncertainty: volatility clusters in turbulent regimes, dissipates in stable periods, and shifts abruptly around structural breaks. Yet many probabilistic forecasting methods estimate predictive uncertainty as an independent per-step quantity, leaving the evolution and persistence of volatility regimes under-modeled. We formalize this missing dimension as temporal uncertainty dynamics and instantiate it in the Volatility Dynamics Variational Autoencoder (VolDy-VAE), a non-autoregressive generative forecaster with a location-scale decoder. VolDy-VAE combines a location path for mean prediction with a recurrent scale path that transfers and evolves a volatility hidden state from the look-back window to the forecasting horizon, enabling temporally coherent predictive variances. This design yields an adaptive attenuation mechanism: high-variance observations receive lower influence on the location estimate while their uncertainty is preserved through explicit scale predictions. We further provide a simplified regime-switching analysis showing that, when variances are known or consistently estimated, the volatility-aware objective reduces to inverse-variance weighting, whereas MSE-based estimators remain unbiased but statistically inefficient. Experiments on nine benchmarks show that VolDy-VAE improves forecasting accuracy and uncertainty calibration over competitive probabilistic and point-forecasting baselines while maintaining low inference latency; plug-in studies further indicate that the VolDy principle can benefit GAN, Koopman VAE, and Transformer backbones. The source code is publicly available at https://github.com/wangyijunlyy/VolDy-VAE.

Abstract

Time Series Forecasting

Explore similar work

Beyond Static Uncertainty: Modeling Temporal Uncertainty Dynamics for Probabilistic Time Series Forecasting

Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series

SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models