Multi-Modal Time Series Prediction via Mixture of Modulated Experts

• URL: http://arxiv.org/abs/2601.21547v1
• Date: Thu, 29 Jan 2026 11:03:09 GMT
• Title: Multi-Modal Time Series Prediction via Mixture of Modulated Experts
• Authors: Lige Zhang, Ali Maatouk, Jialin Chen, Leandros Tassiulas, Rex Ying,
• Abstract summary: We propose Expert Modulation, a new paradigm for multi-modal time series prediction.<n>Our proposed method demonstrates substantial improvements in multi-modal time series prediction.
• Score: 28.358760170766004
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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