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
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. 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
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
The paper introduces Context as Covariates (CoCo), a forecasting framework that combines Large Language Models (LLMs) and Time Series Foundation Models (TSFMs). It generates realistic multimodal datasets by annotating real-world time series with LLMs and aligns the two models through staged fine‑tuning: first using Group Relative Policy Optimization to improve the LLM’s covariate generation, then fine‑tuning the TSFM on those covariates. Experiments on synthetic and real benchmarks across finance, economics, weather, traffic, and security show that CoCo can outperform state‑of‑the‑art TSFMs and LLM forecasters by up to 12% MAE.
By Hemanth Ram, Sai Shankar Narasimhan, Shubhankar Agarwal, Sandeep Chinchali
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.
By Huu Hiep Nguyen, Minh Hoang Nguyen, Dung Nguyen, Hung Le