TimeWorld introduces a plug‑in framework for action‑conditioned multimodal world models in time‑series forecasting. It builds on pretrained time‑series foundation models (TSFMs) and adds a Prototype‑Guided Latent Modulation module to embed textual actions into temporal dynamics, along with an Action Contrastive Learning component to sharpen action‑specific response modeling. The authors also present TSWM‑Bench, a cross‑domain benchmark of seven real‑world datasets with textual interventions, showing that simple text‑prefix adaptations outperform conventional multimodal forecasters and that pretrained TSFMs already encode useful world knowledge for action‑conditioned predictions.
By Hang Ni, Weijia Zhang, Hao Liu
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: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:2610.32657v1 Announce Type: new
Abstract: Decision Transformer (DT) has demonstrated strong performance in various offline reinforcement learning (RL) tasks by conditioning action generation on...
By Changxu Ni, Yiyang Ni, Jun Li, Feiran You, Hongyang Du, Haitao Zhao, Shi Jin
arXiv:2501. 14622v5 Announce Type: replace Abstract: Learning efficient representations for decision-making policies is a challenge in imitation learning (IL).
By Aleksandar Vujinovic, Aleksandar Kovacevic
arXiv:2610.18877v1 Announce Type: new
Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervision...
By Van Dai Do, Huu Hiep Nguyen, Minh Hoang Nguyen, Manh Nguyen, Hung Le
arXiv:2609.17029v1 Announce Type: cross
Abstract: Traditional energy forecasting solutions rely on task-specific supervision and energy asset representations, limiting transferability and the ability...
By Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos, Efstathios Sarantinopoulos, Ionut Anghel, Elissaios Sarmas
NextMe-800 is an approximately 800‑hour first‑person video dataset collected from a single volunteer over 126 days, featuring 1 Hz images, gaze, and audio. The data are captioned at five hierarchical abstraction levels—from atomic actions to major activities—enabling personalized action anticipation as an open‑vocabulary K‑step sequence prediction task. The authors also introduce NextAct, a 1,500‑point benchmark that combines NextMe‑800 with the multi‑person EgoLife dataset, and evaluate models using an embedding‑based soft edit distance to assess how well personal behavior can be anticipated across abstraction levels and prediction horizons.
By Zhaoxu Meng, Yiming Sun, Mingyuan Gao, Jiachang Zhang, Zhuhan Dai, Yipeng Du, Zheng Lian, Jian-Qiao Zhu
arXiv:2607. 19404v1 Announce Type: cross Abstract: Multivariate time series encode structural patterns that unfold across multiple temporal scales, yet most forecasting backbones treat learned representations as transient byproducts of prediction, leaving the organizational geometry of these patterns underexploited.
By Xingsheng Chen, Deyu Yi, Siu-Ming Yiu
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
arXiv:2511. 09789v2 Announce Type: replace Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics.
By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han
arXiv:2507. 23615v2 Announce Type: replace-cross Abstract: Data augmentation is becoming increasingly important across various areas of time series analysis, including forecasting, classification, and anomaly detection.
By Luis Roque, Vitor Cerqueira, Carlos Soares, Luis Torgo