Time series forecasting has advanced rapidly, especially with the emergence of foundation models that show strong zero-shot performance on numerical extrapolation. However, in real-world forecasting settings, a statistically plausible baseline is rarely the final forecast used in practice. Before a forecast becomes decision-ready, it often needs to be revised using weakly structured business context such as holiday effects, campaign plans, external events, historical analogs, and expert feedback. This practical stage remains underexplored in the forecasting literature. In this paper, we formulate this stage as the \textbf{last-mile forecasting} problem and present an LLM-agent framework that sits on top of a forecasting backbone. Our system maintains a unified forecast workspace, invokes tools to retrieve contextual evidence, and converts reasoning trajectories into explicit forecast revision actions under structural safety constraints. It also supports long-horizon forecasting through map-reduce-style decomposition and post-hoc reflection through a memory bank. The resulting system is designed to be controllable and auditable. Through real-world case studies, we show how LLM agents can bridge the gap between statistical prediction and business-ready forecasting.
Bridging the Experimental Last Mile: Digitizing Laboratory Know-How for Safe AI-Assisted Support
Division of Applied Chemistry, Faculty of Engineering, Hokkaido University（北海道大学工学部应用化学科）；Graduate School of Chemical Sciences and Engineering, Hokkaido University（北海道大学研究生院化学科学和工程学系）；Graduate School of Information Science and Technology, Hokkaido University（北海道大学研究生院信息科学和技术学系）；Quantum Nexus, Inc.
Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
School of Data Science, The Chinese University of Hong Kong-Shenzhen（香港中文大学（深圳）数据科学学院）；Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong（香港中文大学机械与自动化工程学系）
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning
State Grid Smart Grid Research Institute Co.Ltd.（国网智能电网研究院有限公司）；Wuhan Nari Limited Liability Company of State Grid Electric Power Research Institute（国网电力科学研究院武汉南瑞有限责任公司）；State Grid Inner Mongolia East Power Co., Ltd（国网内蒙古东部电力有限公司）；Electric Power Research Institute of State Grid Anhui Electric Power Co., Ltd（国网安徽电力科学研究院）；State Grid Anhui Ultra High Voltage Company（国网安徽超高压公司）
James Cook University（詹姆斯库克大学）；College of Science and Engineering（科学与工程学院）
Spatio-temporal sequence forecasting is one of the fundamental tasks in spatio-temporal data mining. It facilitates many real world applications such as precipitation nowcasting, citywide crowd flow prediction and air pollution forecasting. Recently, a few Seq2Seq based approaches have been proposed, but one of the drawbacks of Seq2Seq models is that, small errors can accumulate quickly along the generated sequence at the inference stage due to the different distributions of training and inference phase. That is because Seq2Seq models minimise single step errors only during training, however t…
TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
University of Bristol（布里斯托尔大学）；School of Computer Science, University of Bristol（布里斯托尔大学计算机科学学院）；School of Engineering Mathematics and Technology, University of Bristol（布里斯托尔大学工程数学与技术学院）
From Time Series Analysis to Question Answering: A Survey in the LLM Era
Renmin University of China（中国人民大学）；Tsinghua University（清华大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学（广州））；Aalborg University（奥胡斯大学）
University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）
Real-time traffic accident forecasting is increasingly important for public safety and urban management (e.g., real-time safe route planning and emergency response deployment). Previous works on accident forecasting are often performed on hour levels, utilizing existed neural networks with static region-wise correlations taken into account. However, it is still challenging when the granularity of forecasting step improves as the highly dynamic nature of road network and inherent rareness of accident records in one training sample, which leads to biased results and zero-inflated issue. In this …
QUALS: Corpus Equilibrium for Universal Forecasting via Pattern Quantization and Learnability Synchronization
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所人工智能安全国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）；School of Data Science and Engineering, East China Normal University（华东师范大学数据科学与工程学院）
PLAN-S: Bridging Planning with Latent Style Dynamics for Autonomous Driving World Models
Intelligent Transportation Thrust, Systems Hub, and Center of Seamless Connectivity & Connected Intelligence, The Hong Kong University of Science and Technology (Guangzhou)（智能交通 thrust、系统中心及无缝连接与智能连接研究院，香港科学与技术大学（广州））
OmniPMNet: Bridging discrete and gridded PM10 forecasts via omni-query neural processes
ColorfulClouds Technology Co., Ltd.（彩云科技有限公司）；School of Systems Science, Beijing Normal University（北京师范大学系统科学学院）；D-ITET, ETH Zurich（苏黎世联邦理工学院动态信息与电气工程系）
The Forecast After the Forecast: A Post-Processing Shift in Time Series
Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center (National Supercomputing Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences)（计算机网络与信息安全管理重点实验室，教育部，山东计算机科学中心（济南国家超算中心），齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing, Shandong Fundamental Research Center for Computer Science（山东省计算互联网与服务计算重点实验室，山东省计算机科学基础研究中心）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University（SDU-NTU联合人工智能研究中心（C-FAIR），山东大学）；Department of Computer and Data Sciences, Case Western Reserve University（计算机与数据科学系，凯斯西储大学）；Department of Biomedical Engineering, Case Western Reserve University（生物医学工程系，凯斯西储大学）
NOVA LINCS（新里斯本大学计算机科学与信息学实验室）；Universidade Nova de Lisboa（新里斯本大学）
With the constant increase of available data in various domains, such as the Internet of Things, Social Networks or Smart Cities, it has become fundamental that agents are able to process and reason with such data in real time. Whereas reasoning over time-annotated data with background knowledge may be challenging, due to the volume and velocity in which such data is being produced, such complex reasoning is necessary in scenarios where agents need to discover potential problems and this cannot be done with simple stream processing techniques. Stream Reasoners aim at bridging this gap between …
