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arXiv AI By Lige Zhang, Ali Maatouk, Jialin Chen, Karthik Charan Konduri, Leandros Tassiulas, Rex Ying

Multi-Modal Time Series Prediction via Mixture of Modulated Experts

Read the original on arXiv AI →

The paper introduces Expert Modulation, a novel approach for multi‑modal time series prediction that conditions both expert routing and computation on textual signals, thereby providing direct cross‑modal control over expert behavior. Unlike previous methods that rely on token‑level fusion, this mechanism avoids mixing temporal patches with language tokens in a shared embedding space, which can be problematic when high‑quality time‑text pairs are scarce or when time series characteristics vary widely. Experiments and theoretical analysis demonstrate that Expert Modulation yields strong improvements over existing multi‑modal forecasting techniques.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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