提出ASGMamba模型，高效处理多变量长时间序列预测。
ASGMamba: Adaptive Spectral Gating Mamba for Multivariate Time Series Forecasting
- 引入自适应频谱门控机制，动态过滤高频噪声
- 在9个基准上达最优精度，保持线性复杂度
- 适合资源受限的超算环境使用
长期多变量时间序列预测（LTSF）在实时电网管理、大规模交通流模拟等高性能计算场景中至关重要。现有方法面临两难：基于Transformer的模型因二次复杂度难以扩展至长序列，而线性状态空间模型（SSMs）常无法区分有效信号与高频噪声，导致状态容量浪费。为此，我们提出ASGMamba，一种专为资源受限超算环境设计的高效预测框架。该框架集成轻量级自适应频谱门控（ASG）机制，依据局部频谱能量动态过滤噪声，使Mamba主干更聚焦于稳健的时间动态。同时引入分层多尺度结构与变量特异性节点嵌入，以捕捉不同物理特性。在九个基准上的实验表明，ASGMamba在严格保持$$\mathcal{O}(L)$$复杂度的前提下，显著降低长时序任务内存占用，实现最先进的预测精度，成为资源受限环境下高吞吐预测的可扩展解决方案。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。