| Summary: | Abstract The transformer-based approach excels in long-term series forecasting. These models leverage stacking structures and self-attention mechanisms, enabling them to effectively model dependencies in series data. While some approaches prioritize sparse attention to tackle the quadratic time complexity of self-attention, it can limit information utilization. We introduce a creative double-branch attention mechanism that simultaneously captures intricate dependencies in both temporal and variable perspectives. Moreover, we propose query-independent attention, taking into account the near-identical attention allocated by self-attention to different query positions. This enhances efficiency and reduces the impact of redundant information. We integrate the double-branch query-independent attention into popular transformer-based methods like Informer, Autoformer, and Non-stationary transformer. The results obtained from conducting experiments on six practical benchmarks consistently validate that our novel attention mechanism substantially improves the long-term series forecasting performance in contrast to the baseline approach.
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