多模态时间序列预测 via 专家混合
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
AI总结：
本文提出专家调制方法，通过条件控制专家行为实现多模态时间序列预测的高效跨模态控制。
摘要：
现实世界的时间序列表现出复杂且不断变化的动态，使准确预测极为具有挑战性。最近的多模态预测方法利用新闻报告等文本信息来提高预测精度，但大多数方法依赖于令牌级融合，将时间片段与语言令牌混合在共享嵌入空间中。然而，当高质量的时间-文本对稀缺且时间序列在规模和特征上存在显著差异时，这种融合可能并不合适，从而复杂化了跨模态对齐。同时，专家混合（MoE）架构在时间序列建模和多模态学习中已被证明有效，但许多现有的基于MoE的模态整合方法仍然依赖于令牌级融合。为了解决这一问题，我们提出了专家调制，这是一种新的多模态时间序列预测范式，它将路由和专家计算都基于文本信号进行条件控制，从而实现对专家行为的直接和高效的跨模态控制。通过全面的理论分析和实验，我们提出的方法在多模态时间序列预测方面显示出显著的改进。当前的代码可在https://github.com/BruceZhangReve/MoME上获得。
英文摘要：
Real-world time series exhibit complex and evolving dynamics, making accurate forecasting extremely challenging. Recent multi-modal forecasting methods leverage textual information such as news reports to improve prediction, but most rely on token-level fusion that mixes temporal patches with language tokens in a shared embedding space. However, such fusion can be ill-suited when high-quality time-text pairs are scarce and when time series exhibit substantial variation in characteristics, thus complicating cross-modal alignment. In parallel, mixture-of-experts (MoE) architectures have proven effective for both time series modeling and multi-modal learning, yet many existing MoE-based modality integration methods still depend on token-level fusion. To address this, we propose Expert Modulation, a new mechanism for multi-modal time series prediction that conditions both routing and expert computation on textual signals, enabling direct and efficient cross-modal control over expert behavior. Through theoretical analysis and experiments, our proposed method demonstrates strong improvements in multi-modal time series prediction. The current code implementation is available at https://github.com/BruceZhangReve/MoME
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。
