Time Series Models Evaluated for US Influenza Forecasting

A new research paper evaluates various time series forecasting models for predicting seasonal influenza in the United States. The study found that a mixture-of-experts model, which combines multiple pretrained forecasters, achieved the best performance. Transformer-based models also showed reliability, with pretraining offering the most significant benefits for longer-term predictions, especially when aligned with influenza dynamics. Large language model (LLM) based time series methods, however, underperformed compared to numerical forecasters in this specific application. AI

IMPACT Provides guidance on selecting and applying advanced time series models for public health forecasting.

RANK_REASON Research paper evaluating machine learning models for a specific application. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• influenza
• mixture of experts model
• neural network architectures
• Time Series Foundation Models
• Transformer-based Models
• United States

• paper
• other

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