Mixformer: Feature Mixed Transformer for Rainfall Forecasting

Yuanyuan Liao, Boyuan Li, Xiuhong Li

Abstract: In the Xinjiang region of China, water is scarce and unevenly distributed, and due to factors such as global warming, extreme rainfall events occur frequently, posing serious threats to people's lives and property safety. To accurately predict short-term precipitation, we propose a Mixformer model. Specifically, we first use a combination of min-max normalization and reversible instance normalization to reduce the impact of feature numerical ranges on modeling while preserving the distribution of the original data. Next, to enhance the representation of multivariate time series data, we propose a feature mixing module. This module enhances the representation of inter-feature correlation information by calculating the correlation between different sequences, thus improving prediction effectiveness. Finally, to enhance its non-linear characteristics, we introduce a residual prediction method. This method models seasonal and trend components separately and also pays special attention to the residual component. We have validated the proposed method extensively, proving that it outperforms existing state-of-the-art (SOTA) methods in this field.