AdaMamba：适应性频率门控Mamba用于长期时间序列预测
AdaMamba: Adaptive Frequency-Gated Mamba for Long-Term Time Series Forecasting
AI总结：
AdaMamba通过引入交互补丁编码模块和自适应频率门控状态空间模块，有效整合频率域分析与时间依赖学习，提升长期时间序列预测的准确性和效率。
摘要：
准确的长期时间序列预测（LTSF）需要捕捉复杂的长程依赖和动态周期模式。最近的频域分析方法提供了一种全局视角来揭示时间特征。然而，现实中的时间序列往往表现出显著的跨域异质性，其中在时间域中同步的变量在频域中可能差异很大。现有的基于频域的LTSF方法通常依赖于隐含的跨域同质性假设，限制了其适应复杂变化的能力。为此，我们提出AdaMamba，一种新的框架，将自适应和上下文感知的频域分析内化到Mamba状态空间更新过程中。具体而言，AdaMamba引入了一个交互补丁编码模块来捕捉变量间的交互动态。然后，我们开发了一个自适应频率门控状态空间模块，生成输入依赖的频率基，将传统的时序遗忘门扩展为统一的时间-频率遗忘门。这使得可以根据学习到的频域重要性动态校准状态转移，同时保持Mamba在建模长程依赖方面的能力。在七个公开的LTSF基准和两个领域特定数据集上的广泛实验表明，AdaMamba在预测准确性上始终优于最先进的方法，同时保持了竞争性的计算效率。AdaMamba的代码可在https://github.com/XDjiang25/AdaMamba上获取。
英文摘要：
Time-series forecasting is foundational to a wide spectrum of web-scale services, spanning personalized recommendations, energy scheduling, and infrastructure capacity planning. Accurate long-term time series forecasting (LTSF) requires models to capture long-range temporal dependencies while adapting to periodic patterns that vary across variables. Although frequency-aware methods offer a global view of temporal dynamics, they typically rely on inflexible heuristic designs, such as predefined period lengths, manually selected or randomly sampled top-k frequency components, and separate spectral transformations. Consequently, they fail to account for time-frequency heterogeneity, where variables that appear synchronized in the time domain can differ substantially in the frequency domain. Meanwhile, Mamba-based forecasters, despite their efficiency in long-range modeling, introduce frequency information only as auxiliary signals, leaving spectral evidence disconnected from state transitions. In this work, we propose AdaMamba, a novel approach that explicitly internalizes input-dependent frequency information into the modeling of temporal dependencies. AdaMamba comprises an interaction learning module and an adaptive frequency-gated state space module: the former captures cross-variable dependencies and multi-scale temporal context, while the latter generates context-dependent frequency bases and unifies temporal forgetting into a single time-frequency gate that directly modulates state evolution. Extensive experiments on seven public LTSF benchmarks and four domain-specific datasets demonstrate that AdaMamba consistently outperforms state-of-the-art baselines while maintaining competitive computational efficiency.
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。
