用文本调控专家网络，提升多模态时序预测精度
Multi-Modal Time Series Prediction via Mixture of Modulated Experts
- 文本信号直接控制专家路由与计算，实现跨模态精准调节
- 在多个数据集上相比基线提升显著，最高达18.7%的误差降低
- 适合缺乏高质量时文配对、时序差异大的场景
现实世界的时间序列具有复杂且动态演化的特征，使得精确预测极具挑战。现有基于多模态的预测方法利用新闻等文本信息提升预测效果，但多数依赖于将时间片段与语言令牌在共享嵌入空间中进行词元级融合，当高质量时文配对稀缺且时序数据在尺度和特性上差异显著时，这种融合方式难以实现跨模态对齐。与此同时，专家混合（MoE）架构在时序建模与多模态学习中已证明有效，但多数现有方法仍依赖词元级融合。为此，本文提出专家调制（Expert Modulation），一种新型多模态时序预测范式，通过文本信号同时调控路由与专家计算，实现对专家行为的直接高效跨模态控制。通过系统的理论分析与实验验证，所提方法在多模态时序预测任务中表现出显著性能提升。代码已公开于 https://github.com/BruceZhangReve/MoME。
原文摘要 · Abstract (English)
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 scale and 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 paradigm 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 comprehensive theoretical analysis and experiments, our proposed method demonstrates substantial improvements in multi-modal time series prediction. The current code is available at https://github.com/BruceZhangReve/MoME
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