arXiv Machine Learning

TiMi: Empower Time Series Transformers with Multimodal Mixture of Experts

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.

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.

By Haoxin Liu, Yichen Zhou, Rajat Sen, B. Aditya Prakash, Abhimanyu Das

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.

By Zhi Lei, Chenxi Liu, Hao Miao, Wanghui Qiu, Bin Yang, Chenjuan Guo

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

arXiv AI

1d ago

Adapting LLMs to Time Series Forecasting via Temporal Heterogeneity Modeling and Representation Alignment

arXiv:2508. 07195v2 Announce Type: replace-cross Abstract: Recent advances have demonstrated that Large Language Models (LLMs) can be effectively adapted for time series forecasting, revealing strong potential beyond natural language tasks.

By Yanru Sun, Emadeldeen Eldele, Zongxia Xie, Yucheng Wang, Wenzhe Niu, Qinghua Hu, Chee Keong Kwoh, Min Wu

llms benchmarks safety

arXiv Machine Learning

Jul 16

Overcoming the Modality Gap in Context-Aided Forecasting

arXiv:2603. 12451v4 Announce Type: replace Abstract: Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to surpass traditional statistical methods.

By Vincent Zhihao Zheng, \'Etienne Marcotte, Arjun Ashok, Andrew Robert Williams, Lijun Sun, Alexandre Drouin, Valentina Zantedeschi

multimodal

arXiv AI

Jun 18

From Values to Tokens: An LLM-Driven Framework for Context-aware Time Series Forecasting via Symbolic Discretization

arXiv:2508. 09191v2 Announce Type: replace-cross Abstract: Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance.

By Xiaoyu Tao, Shilong Zhang, Mingyue Cheng, Daoyu Wang, Tingyue Pan, Bokai Pan, Changqing Zhang, Shijin Wang

llms nlp fine-tuning safety

arXiv AI

Jul 17

VLT: A Vision-Language-Time Series Multimodal Foundation Model for Industrial Intelligence

arXiv:2607. 14510v1 Announce Type: new Abstract: Industrial time series serve as the foundation for Prognostics and Health Management (PHM) to ensure the reliability and safety of industrial equipment such as aero-engines.

By Haiteng Wang, Jingheng Yan, Xiaokang Wang, Lei Ren

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 14

CoGenCast: A Coupled Autoregressive-Flow Generative Framework for Time Series Forecasting

arXiv:2602. 03564v2 Announce Type: replace Abstract: Time series forecasting can be viewed as a generative problem that requires both semantic understanding over contextual conditions and stochastic modeling of continuous temporal dynamics.

By Mingyue Cheng, Yaguo Liu, Daoyu Wang, Xiaoyu Tao, Qi Liu

llms diffusion multimodal benchmarks

arXiv Machine Learning

1d ago

Multi-Granularity Sentiment Integration for LLM-Based Multimodal Sentiment Analysis

arXiv:2608. 16201v1 Announce Type: new Abstract: Multimodal sentiment analysis (MSA) aims to predict sentiment polarity and intensity from heterogeneous inputs such as text, audio, and vision.

By Shanshan Lin, Yuesheng Wu, Chao Chen, Yizhe Yang, Zhihao Chen, Zexian Yang, Xiangwen Liao

llms nlp multimodal benchmarks safety

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