DAformer: Transformer with dual-branch aggregation for long-term time series forecasting

Long-term time series forecasting is widely applied in fields such as transportation, energy consumption, and disease control, yet existing Transformer-based methods often struggle to capture complex temporal dependencies and accurately model long-range patterns through a single branch. To address these issues, we propose a Dual-branch Aggregation Method (DAformer) for long-term time series forecasting, designed to enhance prediction accuracy through a modular framework. DAformer consists of three main components: a temporal feature extraction module, a correlation-based feature decomposition module, and a dual-branch aggregation module. The temporal feature extraction module utilizes a self-attention mechanism to capture both short-term and long-term dependencies between time points, regardless of their temporal distance, enabling effective modeling of complex temporal patterns. The correlation-based feature decomposition module decomposes time series into distinct seasonal and trend components and further analyzes key temporal features by evaluating interactions across various time intervals. Finally, the dual-branch aggregation module integrates the features extracted by the two branches, capturing feature similarities and interdependencies across multiple time series. Our method outperforms state-of-the-art (SOTA) methods in terms of predictive ability. It achieves the best forecasting performance across eight datasets. For prediction lengths of 96, 192, 336, and 720, the average MSE values are 0.258, 0.310, 0.375, and 0.499, respectively, with corresponding MAEs of 0.295, 0.334, 0.378, and 0.449.