Performance of a hybrid ARIMA-LSTM-Transformer model in influenza-like illness forecasting: A comparative study

Abstract

Accurate forecasting of influenza-like illness (ILI) incidence is essential for public health surveillance and early epidemic intervention. Traditional statistical models effectively capture linear temporal trends but often fail to model the complex nonlinear dynamics of disease transmission. To address this limitation, this study proposes a hybrid forecasting framework that integrates Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), temporal attention mechanisms, and Transformer architecture. Weekly ILI surveillance data collected from Xinjiang and Shandong provinces, China, between 2023 and 2025 were used for model development and validation. Meteorological variables, including weekly average temperature and relative humidity, were incorporated as exogenous features. The proposed framework decomposes the forecasting task into linear and nonlinear components, where ARIMA captures long-term linear trends and an attention-enhanced LSTM–Transformer branch models nonlinear temporal dependencies. Experimental results demonstrated that the proposed model achieved superior predictive performance on the Xinjiang Production and Construction Corps dataset, with an MAE of 29.371, RMSE of 41.334, MAPE of 10.744%, and R2 of 0.579. Diebold–Mariano tests confirmed statistically significant improvements over standalone ARIMA, LSTM, and ARIMA– LSTM models (P < 0.05). Furthermore, cross-regional validation on the Yantai High-Tech Zone dataset showed comparable forecasting accuracy, indicating strong generalization capability under different climatic conditions. These findings suggest that the proposed hybrid framework effectively captures both linear and nonlinear transmission patterns of ILI and provides a practical tool for early warning and public health resource allocation in grassroots disease surveillance systems.