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Machine Learning and ARIMA Model Averaging for Adaptive Public Health Forecasting: Comparative Evaluation and an Ontario COVID-19 Case Study
Author1, Author2, Author3, Author4, Author5
forecastingensemble methodspublic healthmachine learning
2608.20406
Builder Relevance
2h ago80%
Abstract
This paper evaluates various forecasting models for public health data, highlighting the effectiveness of a new ensemble method called MLAMA.
Reality Card
Core Claim
MLAMA achieved the lowest normalized mean absolute percentage error across most forecast horizons, demonstrating its effectiveness in adaptive public health forecasting.
Method / Result
MLAMA outperformed other models in terms of normalized mean absolute percentage error across various forecast horizons.
Limitations
The accompanying Python package is in a private repository, which may limit accessibility for validation and reproducibility testing.
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