arXiv Machine Learning August 19, 2026 By Jiafeng Lin, Yuxuan Wang, Huakun Luo, Jianmin Wang, Zhongyi Pei

TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

Read the original on arXiv Machine Learning →

arXiv:2602. 21693v2 Announce Type: replace Abstract: Multimodal time series forecasting has garnered significant attention for its potential to provide more accurate predictions than traditional single-modality models by leveraging rich information inherent in other modalities.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 17

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

arXiv:2603. 22372v2 Announce Type: replace-cross Abstract: Recent advances in multimodal learning have motivated the integration of auxiliary modalities such as text or vision into time series (TS) forecasting.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, SoonYoung Lee, Wonbin Ahn

multimodal

arXiv Machine Learning

Jul 9

Rethinking Multimodal Time-Series Forecasting Evaluation

arXiv:2607. 06973v1 Announce Type: new Abstract: We introduce a new context-enriched, multimodal time series forecasting benchmark, TimesX.

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multimodal benchmarks

arXiv Machine Learning

23h ago

SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version

arXiv:2608. 17164v1 Announce Type: new Abstract: Textual context such as news, reports, and logs can provide valuable signals for time series forecasting, especially when future dynamics are driven by external events that are not yet visible in historical values.

By Tuan-Binh Tran, Dat Nguyen Cong, Duc-Trong Le, Thanh Trung Huynh, Tung Kieu

llms agents diffusion multimodal benchmarks

arXiv AI

Aug 7

MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

arXiv:2603. 05997v2 Announce Type: replace-cross Abstract: Irregularly sampled time series (ISTS) are widespread in real-world scenarios, exhibiting asynchronous observations on uneven time intervals across diverse variables.

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llms rag multimodal safety

arXiv AI

Jun 6

TRACE: A Temporal Conditional Estimation for Multimodal Time Series Foundation Models

arXiv:2606. 06285v1 Announce Type: new Abstract: Time series foundation models (TS-FMs) aim to learn generalizable temporal representations that can be adapted to a wide range of downstream tasks.

By Ziwen Kan, Yishuo Chen, Kecheng Li, Andrew Wen, Xiaomeng Wang, Liwei Wang, Jihao Duan, Song Wang, Hongfang Liu, Tianlong Chen

nlp multimodal benchmarks

arXiv AI

Jun 29

Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting

arXiv:2602. 01588v3 Announce Type: replace-cross Abstract: Multimodal time series forecasting is crucial in real-world applications, where decisions depend on both numerical data and contextual signals.

By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le

rag multimodal benchmarks safety

Related stories

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

Rethinking Multimodal Time-Series Forecasting Evaluation

SCENARIODIFF: A Scenario-level Guidance Framework for Multimodal Time Series Forecasting--Extended Version

MM-ISTS: Cooperating Irregularly Sampled Time Series Forecasting with Multimodal Vision-Text LLMs

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Spectral Text Fusion: A Frequency-Aware Approach to Multimodal Time-Series Forecasting