The paper proposes TERN, a machine learning model for forecasting influenza epidemics. It uses a delta-rule fast-weight memory with decay and online adaptation to improve forecasting. Results show TERN outperforms existing models on benchmark datasets. This is relevant to AI agents as it demonstrates a new approach to forecasting in a specific domain, which may be of interest to researchers and developers working on AI applications in healthcare or epidemiology.