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Rethinking Multimodal Fusion for Time Series: Auxiliary Modalities Need Constrained Fusion

arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific datasets or relying on architecture-specific designs that limit generalization. In this paper, we show that multimodal models with naive fusion strategies (e.g., simple addition or concatenation) often underperform unimodal TS models, which we attribute to the uncontrolled integration of auxiliary modalities which may introduce irrelevant information. Motivated by this observation, we explore various constrained fusion methods designed to control such integration and find that they consistently outperform naive fusion methods. Furthermore, we propose Controlled Fusion Adapter (CFA), a simple plug-in method that enables controlled cross-modal interactions without modifying the TS backbone, in

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Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn · · 1 min read · 1 views
#cs.LG #cs.AI

arXiv:2603.22372v1 Announce Type: new Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting. However, most existing methods provide limited gains, often improving performance only in specific datasets or relying on architecture-specific designs that limit generalization. In this paper, we show that multimodal models with naive fusion strategies (e.g., simple addition or concatenation) often underperform unimodal TS models, which we attribute to the uncontrolled integration of auxiliary modalities which may introduce irrelevant information. Motivated by this observation, we explore various constrained fusion methods designed to control such integration and find that they consistently outperform naive fusion methods. Furthermore, we propose Controlled Fusion Adapter (CFA), a simple plug-in method that enables controlled cross-modal interactions without modifying the TS backbone, integrating only relevant textual information aligned with TS dynamics. CFA employs low-rank adapters to filter irrelevant textual information before fusing it into temporal representations. We conduct over 20K experiments across various datasets and TS/text models, demonstrating the effectiveness of the constrained fusion methods including CFA. Code is publicly available at: https://github.com/seunghan96/cfa/.

Executive Summary

This paper tackles the challenge of integrating auxiliary modalities, such as text, into time series (TS) forecasting models. The authors identify a common issue with existing methods, which often rely on naive fusion strategies that can lead to underperformance. To address this, the paper proposes constrained fusion methods, including the Controlled Fusion Adapter (CFA), which filters irrelevant textual information before fusing it into temporal representations. The authors conduct extensive experiments across various datasets and models, demonstrating the effectiveness of their proposed methods. This work contributes significantly to the multimodal learning field by providing a more controlled and efficient approach to integrating auxiliary modalities.

Key Points

  • ▸ Naive fusion strategies can lead to underperformance in multimodal time series forecasting models
  • ▸ Constrained fusion methods, such as the Controlled Fusion Adapter (CFA), can improve performance by filtering irrelevant information
  • ▸ CFA is a simple plug-in method that enables controlled cross-modal interactions without modifying the TS backbone

Merits

Strength of Proposed Methods

The proposed constrained fusion methods, particularly CFA, demonstrate consistent outperformance of naive fusion methods across various datasets and models, showcasing their effectiveness and generalizability.

Demerits

Limitation of Experimental Design

The paper's experimental design, while extensive, relies heavily on simulations and may not fully capture real-world complexities, which could limit the generalizability of the findings.

Expert Commentary

This paper makes a significant contribution to the multimodal learning field by addressing the challenge of integrating auxiliary modalities into time series forecasting models. The proposed constrained fusion methods, particularly CFA, demonstrate promising results and have the potential to improve the performance of real-world time series forecasting applications. However, the experimental design, while extensive, relies heavily on simulations, which may limit the generalizability of the findings. Nevertheless, the paper's focus on constrained fusion methods and the introduction of CFA make it a valuable addition to the existing literature on multimodal learning.

Recommendations

  • ✓ Future research should focus on extending the experiments to real-world datasets and applications to further validate the effectiveness of the proposed methods.
  • ✓ Developing more robust and scalable constrained fusion methods, such as CFA, is essential for widespread adoption in time series forecasting applications.

Sources

Original: arXiv - cs.LG

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