DPANet通过双金字塔结构，同时捕捉时间多尺度与频域多分辨率特征。
DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting
- 构建时间金字塔与频域金字塔并行处理多尺度动态与周期性
- 跨金字塔融合块实现粗到细的层次化信息交互，提升预测精度
- 在多个公开数据集上超越现有模型，适合长时序预测场景
长期时间序列预测（LTSF）面临建模跨多时间尺度和频率分辨率复杂依赖关系的挑战。现有方法如Transformer和MLP模型难以统一、结构化地捕捉这些交织特征。我们提出双金字塔注意力网络（DPANet），显式解耦并并行建模时间多尺度动态与频域多分辨率周期性。DPANet构建两个并行金字塔：基于逐步下采样的时间金字塔，以及基于带通滤波的频域金字塔。模型核心是跨金字塔融合块，通过交叉注意力在对应层级间实现深度、交互式的信息交换。该融合遵循从粗到细的层次结构，使全局上下文引导局部表征学习。在多个公开基准上的大量实验表明，DPANet达到当前最优性能，显著优于先前模型。代码已开源：https://github.com/hit636/DPANet。
原文摘要 · Abstract (English)
Long-term time series forecasting (LTSF) is hampered by the challenge of modeling complex dependencies that span multiple temporal scales and frequency resolutions. Existing methods, including Transformer and MLP-based models, often struggle to capture these intertwined characteristics in a unified and structured manner. We propose the Dual Pyramid Attention Network (DPANet), a novel architecture that explicitly decouples and concurrently models temporal multi-scale dynamics and spectral multi-resolution periodicities. DPANet constructs two parallel pyramids: a Temporal Pyramid built on progressive downsampling, and a Frequency Pyramid built on band-pass filtering. The core of our model is the Cross-Pyramid Fusion Block, which facilitates deep, interactive information exchange between corresponding pyramid levels via cross-attention. This fusion proceeds in a coarse-to-fine hierarchy, enabling global context to guide local representation learning. Extensive experiments on public benchmarks show that DPANet achieves state-of-the-art performance, significantly outperforming prior models. Code is available at https://github.com/hit636/DPANet.
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