Novel methods for adaptive time-series forecasting and prediction-interval construction

Abstract We propose novel methods for adaptive series forecasting and prediction-interval construction, illustrated with COVID-19 case and death counts. Our framework applies an automated transformation to reduce heteroscedasticity, then imposes a constrained smoothing near the forecast edge via robust quadratic regression, emphasizing recent data. A Long Short-Term Memory (LSTM) model combined with ARIMA-based noise correction further refines the forecast. Compared to conventional methods (e.g., ARIMA alone, unprocessed deep learning), this adaptive approach achieves superior metrics and reliable bootstrap-derived confidence and prediction intervals. We also highlight how reinforcement learning (RL) can offer promising avenues for real-time decision-making and further improvements in forecasting adaptability.