The paper investigates whether multimodal time‑series forecasting models actually use the semantic content of accompanying text. By systematically perturbing the text—replacing it with empty, constant, shuffled, or cross‑domain sentences—the authors find that mean squared error changes by less than 0.5 % across several architectures, indicating that text does not drive performance gains. They also show that removing a co‑shipped numeric column restores the reported improvements, suggesting that the models rely on other signals rather than textual semantics.
By Karthik Sridhar, Atharva Gupta, Nishant Pradhan, Murari Mandal, Dhruv Kumar, Saurabh Deshpande
The paper introduces a synthetic benchmark for multimodal time‑series forecasting that evaluates how well text annotations contribute to predictions. By generating controlled signals with semantically correct, incorrect, and irrelevant annotations, the authors can precisely measure the true information content. Six mutual‑information estimators (KSG, MINE, InfoNCE, CCA, PID, and V‑information) are tested, all correctly ranking useful annotations and enabling annotation auditing without model training. The benchmark also highlights each estimator’s limitations and validates findings on seven real datasets, providing practical guidelines for metric implementation.
By Emma Andrews, Gianmarco Mengaldo
arXiv:2609.24156v1 Announce Type: cross
Abstract: Most existing time series forecasting methods rely solely on numerical observations, overlooking rich contextual information from auxiliary texts. Re...
By Jiayi Liang, Xiaotian Gu, Xinyu Xie, Yuanbin Wu, Xiaoling Wang
arXiv:2606. 14941v1 Announce Type: new Abstract: Time series forecasting models often benefit from historical patterns.
By Shiqiao Zhou, Zipeng Wu, Holger Sch\"oner, Edouard Fouch\'e, IAG Wilson, Shuo Wang
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
The paper introduces TiMi, a framework that enhances time series transformers with a Multimodal Mixture-of-Experts (MMoE) module to incorporate multimodal data, especially textual information, into forecasting. TiMi leverages large language models to generate future inferences that guide predictions, eliminating the need for explicit representation alignment. Experiments show TiMi achieves state‑of‑the‑art performance on sixteen real‑world multimodal forecasting benchmarks, outperforming advanced baselines while maintaining adaptability and interpretability.
By Jiafeng Lin, Yuxuan Wang, Huakun Luo, Jianmin Wang, Zhongyi Pei
Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal. This requires both reliable numerical forecasting and the ability to interpret contextual information.
arXiv:2609.15087v1 Announce Type: cross
Abstract: Most time series forecasting benchmarks remain numerical-centric and provide limited support for evaluating contextual information that shapes real-w...
By Peng Chen, Zhihao Zhuang, Hongzhou Chen, Junhao Huang, Aiping Yang, Mengsen Wu, Yiding Liu, Xilin Dai, Zewei Dong
arXiv:2607. 24892v1 Announce Type: cross Abstract: Text-conditioned time-series forecasting predicts a series from both its numerical history and natural-language context, allowing forecasts to account for events and constraints that the past alone cannot reveal.
By Huu Hiep Nguyen, Dung Nguyen, Minh Hoang Nguyen, Dai Do, Hung Le
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
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
arXiv:2602.01605v2 Announce Type: replace
Abstract: Time Series Foundation Models (TSFMs) leverage extensive pretraining to accurately predict unseen time series during inference, without the need fo...
By Anthony Bao, Venkata Hasith Vattikuti, Jeffrey Lai, William Gilpin