A General Framework for Uncertainty Quantification via Neural SDE-RNN

• URL: http://arxiv.org/abs/2306.01189v1
• Date: Thu, 1 Jun 2023 22:59:45 GMT
• Title: A General Framework for Uncertainty Quantification via Neural SDE-RNN
• Authors: Shweta Dahale, Sai Munikoti, Balasubramaniam Natarajan
• Abstract summary: Uncertainty quantification is a critical yet unsolved challenge for deep learning. We propose a novel framework based on the principles of recurrent neural networks and neural differential equations for reconciling irregularly sampled measurements. Our experiments on the IEEE 37 bus test system reveal that our framework can outperform state-of-the-art uncertainty quantification approaches for time-series data imputations.
• Score: 0.3314882635954751
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for reconciling irregularly sampled measurements. We impute measurements at any arbitrary timescale and quantify the uncertainty in the imputations in a principled manner. Specifically, we derive analytical expressions for quantifying and propagating the epistemic and aleatoric uncertainty across time instants. Our experiments on the IEEE 37 bus test distribution system reveal that our framework can outperform state-of-the-art uncertainty quantification approaches for time-series data imputations.

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