Self-Attention-Based Deep Learning Network for Regional Influenza Forecasting

Abstract

Early prediction of influenza plays an important role in minimizing the damage caused, as it provides the resources and time needed to formulate preventive measures. Compared to traditional mechanistic approach, deep/machine learning-based models have demonstrated excellent forecasting performance by efficiently handling various data such as weather and internet data. However, due to the limited availability and reliability of such data, many forecasting models use only historical occurrence data and formulate the influenza forecasting as a multivariate time-series task. Recently, attention mechanisms have been exploited to deal with this issue by selecting valuable data in the input data and giving them high weights. Particularly, self-attention has shown its potential in various forecasting tasks by utilizing the predictive relationship between objects from the input data describing target objects. Hence, in this study, we propose a forecasting model based on self-attention for regional influenza forecasting, called SAIFlu-Net. The model exploits a long short-term memory network for extracting time-series patterns of each region and the self-attention mechanism to find the similarities between the occurrence patterns. To evaluate its performance, we conducted extensive experiments with existing forecasting models using weekly regional influenza datasets. The results show that the proposed model outperforms other models in terms of root mean square error and Pearson correlation coefficient.