MODE：基于低秩神经ODEs增强的高效时间序列预测
MODE: Efficient Time Series Prediction with Mamba Enhanced by Low-Rank Neural ODEs
AI总结：
MODE通过整合低秩神经ODEs与增强Mamba架构，实现了高效的时间序列预测，提升了准确性和可扩展性。
摘要：
时间序列预测在金融、医疗、能源系统和环境建模等多个领域中发挥着关键作用。然而，现有方法在平衡效率、可扩展性和准确性方面往往面临挑战，尤其是在处理长距离依赖性和不规则采样数据时。为了解决这些挑战，我们提出了MODE，一个集成了低秩神经ODEs与增强Mamba架构的统一框架。如我们的框架所示，输入序列首先通过线性标记层进行转换，然后经过多个Mamba编码器块处理，每个块配备增强的Mamba层，该层采用因果卷积、SiLU激活和低秩神经ODE增强来高效捕捉时间动态。这种低秩形式减少了计算开销同时保持了表达能力。此外，受伪ODE动态启发的分段选择性扫描机制，能够自适应地聚焦于显著的子序列以提高可扩展性和长距离序列建模能力。在基准数据集上的广泛实验表明，MODE在预测准确性和计算效率上均优于现有基线。总体而言，我们的贡献包括：（1）一个统一且高效的长周期时间序列建模架构；（2）将Mamba的选择性扫描与低秩神经ODEs结合以增强时间表示；（3）通过低秩近似和动态选择性扫描实现的显著效率和可扩展性提升。
英文摘要：
Time series prediction plays a pivotal role across diverse domains such as finance, healthcare, energy systems, and environmental modeling. However, existing approaches often struggle to balance efficiency, scalability, and accuracy, particularly when handling long-range dependencies and irregularly sampled data. To address these challenges, we propose MODE, a unified framework that integrates Low-Rank Neural Ordinary Differential Equations (Neural ODEs) with an Enhanced Mamba architecture. As illustrated in our framework, the input sequence is first transformed by a Linear Tokenization Layer and then processed through multiple Mamba Encoder blocks, each equipped with an Enhanced Mamba Layer that employs Causal Convolution, SiLU activation, and a Low-Rank Neural ODE enhancement to efficiently capture temporal dynamics. This low-rank formulation reduces computational overhead while maintaining expressive power. Furthermore, a segmented selective scanning mechanism, inspired by pseudo-ODE dynamics, adaptively focuses on salient subsequences to improve scalability and long-range sequence modeling. Extensive experiments on benchmark datasets demonstrate that MODE surpasses existing baselines in both predictive accuracy and computational efficiency. Overall, our contributions include: (1) a unified and efficient architecture for long-term time series modeling, (2) integration of Mamba's selective scanning with low-rank Neural ODEs for enhanced temporal representation, and (3) substantial improvements in efficiency and scalability enabled by low-rank approximation and dynamic selective scanning.
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。
