When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecasting
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Computer Science > Machine Learning
arXiv:2608.25128 (cs)
[Submitted on 25 Aug 2026]
Title:When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecasting
Authors:Ruizhe Zhou, Gaoyuan Du, Xiaoyang Liu, Haoqi Yao, Deepayan Chakrabarti, Jiating Lin, Yixuan Shen
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Abstract:Multi-modal time series forecasting methods integrate auxiliary context into temporal predictions through increasingly sophisticated fusion mechanisms. A growing body of work reports substantial gains, yet it is often unclear whether they reflect genuine use of the context or incidental architectural effects. We ask a narrower, checkable question: when can auxiliary context help a forecaster at all?
We identify two dataset-level conditions that must both hold: (1) the target is not dominated by a last-value shortcut (low autocorrelation rho_h), and (2) the context carries information about the target beyond history (non-zero conditional mutual information delta; when delta=0 no predictor can benefit---a distribution-free result). Through controlled experiments on MoME (a 14.3B-parameter mixture-of-experts model, 6 datasets, 10 seeds) and four additional fusion mechanisms implemented within a single-backbone testbed (5 datasets), we find that when both conditions hold, text-conditioned expert modulation contributes a sizeable MSE reduction; when either fails, the contribution collapses to the capacity floor of the modulation pathway and carries no context-attributable signal.
We establish causality through two interventions: adding a shortcut to MoME suppresses routing contribution by 77-93% across 3 datasets; progressively corrupting context quality drives the context-specific benefit from +44% to negative. We validate the autocorrelation component of our diagnostic on 27 Monash Archive datasets. We provide a calibrated pre-training diagnostic that, on the datasets we test, yields no false positives in well-powered settings. We are explicit about the asymmetry of our evidence: the negative arm is broadly reliable, while the large positive magnitudes come from a single model family (MoME) and are corroborated only in direction by the testbed.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Applications (stat.AP) |
| Cite as: | arXiv:2608.25128 [cs.LG] |
| (or arXiv:2608.25128v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2608.25128
arXiv-issued DOI via DataCite (pending registration)
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