Neural Network-Based Uncertainty Quantification: A Survey of Methodologies and Applications

Abstract

Uncertainty quantification plays a critical role in the process of decision making and optimization in many fields of science and engineering. The field has gained an overwhelming attention among researchers in recent years resulting in an arsenal of different methods. Probabilistic forecasting and in particular prediction intervals (PIs) are one of the techniques most widely used in the literature for uncertainty quantification. Researchers have reported studies of uncertainty quantification in critical applications such as medical diagnostics, bioinformatics, renewable energies, and power grids. The purpose of this survey paper is to comprehensively study neural network-based methods for construction of prediction intervals. It will cover how PIs are constructed, optimized, and applied for decision-making in presence of uncertainties. Also, different criteria for unbiased PI evaluation are investigated. The paper also provides some guidelines for further research in the field of neural network-based uncertainty quantification.

Keywords

• Prediction interval
• uncertainty quantification
• heteroscedastic uncertainty
• neural network
• forecast
• time series data