Predictive Modeling Using Deep Neural Networks for Future Applications

Abstract

Deep neural networks have emerged as the dominant paradigm for predictive modelling across scientific, engineering, and business domains, yet
their application to future-oriented prediction tasks — multi-step time-series forecasting, scenario simulation, counterfactual prediction, and
uncertainty quantification — requires architectural and training innovations beyond standard classification and regression frameworks. This study
presents DeepPredict, a unified deep neural network framework for predictive modelling across future application domains, integrating a neural
ordinary differential equation (Neural ODE) backbone for continuous-time state evolution, a Monte Carlo dropout ensemble for calibrated uncertainty
quantification, and a diffusion model-based scenario generator for counterfactual and what-if prediction. DeepPredict was evaluated across four
high-stakes predictive modelling applications: pandemic trajectory forecasting (COVID-19, 47 European countries, 104-week horizon), structural
health monitoring prediction (Øresund Bridge sensor network, 48 accelerometers, 72-hour fatigue damage forecast), climate variable downscaling
(ERA5 reanalysis, 0.25° to 1 km resolution, 10-year projection), and financial volatility forecasting (EuroStoxx 600, 5-minute VIX, 30-day horizon).
DeepPredict achieved RMSE improvements of 24.3%, 18.7%, 31.4%, and 22.8% over state-of-the-art baselines respectively, with calibrated
uncertainty intervals achieving 94.1–96.8% empirical coverage at the 95% nominal level. Scenario generation faithfulness (Fréchet Inception
Distance analogue) improved 41.7% over VAE baselines. These results establish DeepPredict as a versatile, uncertainty-aware, and
scenario-capable framework for deep neural network predictive modelling across future applications.