Deep Learning–driven Decision Support System for characterization of probability distribution tails

A novel, fully automated Deep Learning-based DSS that classifies an input dataset into ten distinct probability distributions viz. Lognormal (Sub-exponential case with α>1), Lognormal (Hyper-exponential case with α<1), Weibull (Sub-exponential case with α<1), Weibull (Hyper-exponential case with α>1), Gamma (Sub-exponential case with α<1), Gamma (Hyper-exponential case with α>1), Pareto, Exponential, Normal and Log-Pearson III. The framework integrates seven task-specific Deep Neural Network models trained on concentration profiles, concentration-adjusted expected shortfall, Zenga curves, Hill ratio plots, and Zipf plots.