SOURCE-LINKED INTELLIGENCE

When Does Context Routing Help? A Systematic Study of Multi-Modal Fusion in Time Series Forecasting

arXiv · AI, language, vision and robotics · article · Aug 25, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global