Hugging Face Trending Papers

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

arXiv Machine Learning

Sep 1

A Human-in-the-Loop Autonomous Agent for Industry Time Series Forecasting

arXiv:2608.30976v1 Announce Type: new Abstract: Real-world time-series forecasting is rarely a one-shot model invocation: practitioners must formulate tasks, connect data and models, incorporate doma...

By Xiaoyu Tao, Mingyue Cheng, Ze Guo, Bokai Pan, Qi Liu, Shijin Wang, Enhong Chen

llms agents

arXiv AI

Jun 2

Bridging the Last Mile of Time Series Forecasting with LLM Agents

arXiv:2606. 02497v1 Announce Type: new Abstract: Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation.

By Yuhua Liao, Zetian Wang, Qiangqiang Nie, Zhenhua Zhang

llms agents safety

arXiv AI

Aug 25

LLM-based Agents for Forecasting and Prediction: Methods, Training, Evaluation, and Applications

arXiv:2608.23058v1 Announce Type: new Abstract: Large language models (LLMs) now support forecasting systems that combine language-based reasoning with temporal data, evidence retrieval, external too...

By Xiaogang Xu, Jiaqi Tang, Jianmin Chen, Yingying Yan, Zhenchao Tang, Xiangxin Zhou, Xiaobin Hu, Wei Wei, Jinfeng Wu, Qifeng Chen, Lu Zhou, Jiafei Wu, Zhe Liu, Jianwei Yin, Weimin Zheng

llms rag agents benchmarks

arXiv AI

Aug 5

Traceable Multi-Agent System for Knowledge-Based Forecasting

arXiv:2608. 03339v1 Announce Type: new Abstract: Enterprise forecasting increasingly relies on autonomous agents that interpret documents, search for data, generate code, and revise models.

By Junhyeok Kang, Sangjun Han, Hyeokjun Choe, Soonyoung Lee

agents

arXiv Machine Learning

Jul 28

HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows

arXiv:2607. 23983v1 Announce Type: cross Abstract: Operational flood forecasting depends on tacit forecaster expertise that is difficult to formalize, audit, and transfer.

By Qingyi Yang, Siqian Qiu, Bing Li, Xu Shan, Jia Feng, Shunan Zhou, Xudong Zhou, Tiantian Xing, Jiale Guo, Xiaoyi Dong, Gaoyu Liu, Xiaohuan Liu, Haiqing Pu, Qingwen Deng, Xun Zhang, Zhongrun Xiang, Haiyang Qian, Ying Yan, Yongkang Xu, Nuo Lei, Tianlong Jia, Baoying Shan, Carlo De Michele

llms agents benchmarks

arXiv AI

Aug 5

CastFSR: A Fast--Slow--Reflect Agentic Reasoning Framework for Context-Aware Time Series Forecasting

arXiv:2608. 03031v1 Announce Type: new Abstract: Time series forecasting is fundamental to decision-making in complex systems, where future dynamics are influenced not only by historical observations but also by evolving contextual features.

By Xiaoyu Tao, Mingyue Cheng, Bokai Pan, Chuang Jiang, Huanjian Zhang, Tian Gao, Yaguo Liu, Qi Liu, Enhong Chen

llms agents reinforcement-learning fine-tuning

arXiv AI

Jun 2

TimeSage-MT: A Multi-Turn Benchmark for Evaluating Agentic Time Series Reasoning

arXiv:2606. 01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains.

By Yaxuan Kong, Qingren Yao, Yuqi Nie, Yichen Li, Yilei Shao, Stefan Zohren, Anna Vettoruzzo, Joaquin Vanschoren, Ming Jin, Qingsong Wen

llms agents benchmarks

arXiv Machine Learning

Jul 31

Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang

llms agents benchmarks

arXiv Machine Learning

Jun 2

Human in the Loop Adaptive Optimization for Improved Time Series Forecasting

arXiv:2505. 15354v2 Announce Type: replace Abstract: Time series forecasting models often produce systematic, predictable errors even in critical domains such as energy, finance, and healthcare.

By Malik Tiomoko, Hamza Cherkaoui, Giuseppe Paolo, Zhang Yili, Yu Meng, Zhang Keli, Hafiz Tiomoko Ali

llms reinforcement-learning benchmarks

arXiv AI

Jun 11

WorldReasoner: Evaluating Whether Language Model Agents Forecast Events with Valid Reasoning

arXiv:2606. 11816v1 Announce Type: cross Abstract: Forecasting real-world events requires language-model agents to reason under uncertainty from incomplete, time-bounded information.

By Yizhou Chi, Eric Chamoun, Zifeng Ding, Andreas Vlachos

llms agents benchmarks

arXiv AI

Jun 18

ForecastBench-Sim: A Simulated-World Forecasting Benchmark

arXiv:2606. 18686v1 Announce Type: new Abstract: Forecasting benchmarks for general-purpose AI systems usually inherit the constraints of the real world: outcomes resolve slowly, tail events are rare, and counterfactual questions are difficult to score.

By Jaeho Lee, Nick Merrill, Ezra Karger

benchmarks

arXiv Machine Learning

Jun 5

Harnessing Generalist Agents for Contextualized Time Series

arXiv:2606. 05404v1 Announce Type: cross Abstract: Time series are often embedded in rich contexts that are essential for holistic modeling.

By Zihao Li, Kaifeng Jin, Yuanchen Bei, Jiaru Zou, Avaneesh Kumar, Xuying Ning, Yanjun Zhao, Mengting Ai, Baoyu Jing, Hanghang Tong, Jingrui He

llms agents multimodal benchmarks

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