Trend-based Dynamic Selection of Machine Learning Models for Pandemic Time Series Forecasting - M. e Menezes, Eduardo H. X. de; Sales, Jair P. de; Mattos Neto, Paulo S. G. de (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2025: Anais do XXII Encontro Nacional de Inteligência Artificial e Computacional, 2025)
Pandemic time series forecasting is challenging due to rapidly changing patterns, which general-purpose predictors often fail to capture. Recent studies suggest that specialized models are better suited for such complexity, especially when trend changes are key. This work proposes a forecasting approach that dynamically selects specialized Support Vector Regression (SVR) models based on trend classification (SVRTC). SVRTC maintains a pool of models, each tailored to exponential growth, plateau, or decline patterns. Using COVID-19 case series from eight countries, SVRTC was compared to traditional forecasting and existing dynamic selection methods. It achieved remarkable performance among traditional Machine Learning models and competitive results against state-of-the-art dynamic selection, with low computational cost, indicating strong potential for broader application.