DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting
This addresses the challenge of capturing complex dependencies across multiple temporal scales and frequencies in time series forecasting, which is important for applications like finance, energy, and healthcare, though it appears incremental relative to existing attention-based methods.
The paper tackles the problem of long-term multivariate time series forecasting by proposing DPANet, a novel architecture that explicitly models temporal multi-scale dynamics and spectral multi-resolution periodicities in parallel pyramids, achieving state-of-the-art performance on public benchmarks.
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.