Author

Ye Li

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#machine learning Preprint Aug 2026

Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study

These findings support selecting forecasting models according to operating conditions rather than relying on a single universally preferred approach, and provide a practical framework for combining complementary statistical and machine-learning forecasts.

Yu Zou, Ye Li, Johra Moosa et al. · 0 citations

Aug 2026

A Machine Learning-Based Probabilistic Electricity Load Forecasting Method under Extreme Weather Conditions

An integrated probabilistic forecasting framework with three linked stages: extreme-weather load identification, TimeGAN-based sample augmentation, and conformal quantile forecasting, which improves forecasting accuracy under extreme-weather conditions.

Hao Zhang, Xi-Yang Liu, Ruotian Gao et al. · International journal of pat... · 0 citations