A comparative study of time series foundation models for hand, foot, and mouth disease forecasting: TimesFM, Moirai, and traditional approaches.

Background: Hand, foot, and mouth disease (HFMD) is a pediatric infectious disease prevalent in the Asia-Pacific region, requiring accurate forecasting for effective public health interventions. This study aims to compare the performance of time series foundation models (TimesFM and Moirai) with traditional methods (ARIMA and LSTM) in predicting HFMD outbreaks across various datasets and forecasting horizons. Methods: The study analyzed weekly HFMD incidence data from Korea (2015-2024), Singapore (2012-2018), and Chongqing, China (2015-2024). Zero-shot versions of TimesFM (200 M and 500 M) and Moirai models were assessed against ARIMA and LSTM using forecasting horizons of 1 week, 5 weeks, and 10 weeks. Lookback windows of 50 and 100 weeks were used across experiments. Performance was evaluated based on forecasting accuracy across all datasets. Computational resource requirements were also analyzed. Results: For 1-step predictions, ARIMA and Moirai delivered comparable results. TimesFM-500 M achieved the best performance for 5-step predictions with 100-week lookback windows across all datasets. For 10-step predictions, TimesFM-200 M performed well with 50-week lookback windows but showed weaker results with longer historical data. Foundation models demonstrated the potential for robust HFMD forecasting but required greater computational resources. Conclusion: Time series foundation models can effectively predict HFMD outbreaks. While these models require more computational resources, their zero-shot capabilities simplify the forecasting process by eliminating the need for retraining.