WorldTS is a new forecasting framework that models latent dynamics conditioned on multimodal covariates to improve time‑series prediction. It uses a two‑stage training process: first learning latent state dynamics from historical data and covariates, then training a decoder to map predicted latent states back to future observations. Experiments on 21 real‑world datasets demonstrate the effectiveness of this approach.
By Yuhan Zhu, Xiangfei Qiu, Hanyin Cheng, Wangmeng Shen, Chenjuan Guo, Bin Yang, Jilin Hu, Christian S. Jensen
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
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
arXiv:2610.17856v2 Announce Type: cross
Abstract: Time-series analysis is increasingly studied in multimodal settings, yet most benchmarks pair temporal signals with text or with visualizations deriv...
By Ziyang Zhang, Shenyi Li, Yilin Wang, Ziyun Cui, Bowen Zhou, Wen Wu, Chao Zhang
The paper introduces ACTS-WM, a self‑supervised framework that models latent actions underlying time‑series transitions. It combines a Joint‑Embedding Predictive Architecture encoder with an action‑conditioned world model to predict future representations conditioned on inferred latent actions and task information. Experiments on 22 datasets across four tasks show that ACTS‑WM improves downstream performance compared to pretrained JEPA‑based encoders.
By Chia-Han Kuo, Chengshuai Zhao, Huan Liu, Lo Pang-Yun Ting
The paper introduces LLM-Integrated Bayesian State Space Models (LBS) for multimodal time-series forecasting, combining a state space model backbone with a pretrained large language model to handle both numerical and textual data. LBS allows flexible lookback and forecast windows, principled uncertainty quantification, and improved temporal generalization. Experiments on the TextTimeCorpus benchmark show a 13.20% performance gain over the previous state‑of‑the‑art while providing human‑readable forecast summaries.
By Sungjun Cho, Changho Shin, Suenggwan Jo, Xinya Yan, Shourjo Aditya Chaudhuri, Frederic Sala