ASGMamba: 适应性频谱门控Mamba用于多变量时间序列预测
ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecasting
AI总结：
ASGMamba通过自适应频谱门控机制和分层多尺度架构，实现多变量时间序列预测的高精度与低资源消耗。
摘要：
长期多变量时间序列预测（LTSF）在各种高性能计算应用中扮演着关键角色，包括实时能源电网管理和大规模交通流模拟。然而，现有解决方案面临两难困境：基于Transformer的模型存在二次复杂度，限制了其在长序列上的可扩展性；而线性状态空间模型（SSMs）往往难以区分有价值的信号与高频噪声，导致状态容量浪费。为解决这一问题，我们提出了ASGMamba，一种为资源受限超级计算环境设计的高效预测框架。ASGMamba集成了轻量级的自适应频谱门控（ASG）机制，该机制根据局部频谱能量动态过滤噪声，使Mamba主干能够将状态演化聚焦于稳健的时间动态。此外，我们引入了分层多尺度架构与变量特定的节点嵌入，以捕捉多样的物理特性。在九个基准测试中，ASGMamba实现了最先进的准确性。在保持严格$$\mathcal{O}(L)$$复杂度的同时，我们显著减少了长周期任务的内存使用，从而建立了ASGMamba作为资源受限环境下的可扩展高吞吐预测解决方案。代码可在https://github.com/hit636/ASGMamba获取。
英文摘要：
Long-term multivariate time series forecasting (LTSF) plays a crucial role in various high-performance computing applications, including real-time energy grid management and large-scale traffic flow simulation. However, existing solutions face a dilemma: Transformer-based models suffer from quadratic complexity, limiting their scalability on long sequences, while linear State Space Models (SSMs) often struggle to distinguish valuable signals from high-frequency noise, leading to wasted state capacity. To bridge this gap, we propose ASGMamba, an efficient forecasting framework designed for resource-constrained supercomputing environments. ASGMamba integrates a lightweight Adaptive Spectral Gating (ASG) mechanism that dynamically filters noise based on local spectral energy, enabling the Mamba backbone to focus its state evolution on robust temporal dynamics. Furthermore, we introduce a hierarchical multi-scale architecture with variable-specific Node Embeddings to capture diverse physical characteristics. Extensive experiments on nine benchmarks demonstrate that ASGMamba achieves state-of-the-art accuracy. While keeping strictly $$\mathcal{O}(L)$$ complexity we significantly reduce the memory usage on long-horizon tasks, thus establishing ASGMamba as a scalable solution for high-throughput forecasting in resource limited environments.The code is available at https://github.com/hit636/ASGMamba
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
