Does Text Actually Help? Uncovering and Resolving Text Collapse in Multimodal Time Series Forecasting
arXiv:2606.19413v1 Announce Type: new Abstract: Multimodal time series forecasting, which pairs numerical sequences with domain-relevant textual reports, promises to inject world knowledge into forecasting pipelines. However, we uncover a critical failure mode in existing frameworks that we term text collapse: the text branch converges to a content-independent transformation, contributing negligible discriminative signal regardless of the input description. We argue that text collapse is a consequence of a fundamental asymmetry in time series forecasting: the numerical input is strongly autoco
The increasing integration of multimodal data in AI, especially for time series, makes identifying and resolving fundamental failure modes like 'text collapse' critical for advancing practical applications.
This research highlights a significant technical challenge in multimodal AI, suggesting that current approaches may not be effectively leveraging rich textual data, thereby impacting the robustness and accuracy of forecasting models.
The understanding of how textual and numerical data interact in multimodal time series forecasting is refined, promoting development of more robust models that genuinely leverage text for 'world knowledge' injection.
- · AI researchers in multimodal learning
- · Developers of forecasting systems
- · Industries relying on time series predictions (e.g., finance, supply chain)
- · Overly simplistic multimodal AI frameworks
- · Applications that rely on naive text integration for forecasting
More sophisticated architectures for integrating text in time series forecasting will emerge.
Improved forecasting accuracy using multimodal data could lead to better decision-making in various sectors.
The ability to truly inject 'world knowledge' via text could unlock new capabilities in autonomous systems and predictive analytics.
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Read at arXiv cs.LG