The field of electricity price forecasting has seen significant advances in the last years, including the development of new, more accurate forecast models. These models leverage statistical relationships in previously observed data to predict the future; however, there is a lack of analysis explaining these models, which limits their real world applicability in critical infrastructure. In this paper, using data from the Belgian electricity markets, we explore a state-of-the-art forecasting model to understand if its predictions can be trusted in more general settings than the limited context it is trained in. If the model produces poor predictions in extreme conditions or if its predictions are inconsistent with reality, it cannot be relied upon in real-world where these forecasts are used in downstream decision-making activities. Our results show that, despite being largely accurate enough in general, even state of the art forecasts struggle with remaining consistent with reality.
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
School of Computing, University of Connecticut（康奈尔大学计算机学院）；Department of Machine Learning Research, Morgan Stanley（摩根士丹利机器学习研究部）；Ant Group（蚂蚁集团）；TWG Global（TWG全球）
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
University of Houston（德克萨斯大学休斯顿分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Connecticut（康涅狄格大学）；Florida International University（佛罗里达国际大学）；Squirrel Ai Learning（squirrel ai 学习）
DisenTS: Disentangled Channel Evolving Pattern Modeling for Multivariate Time Series Forecasting
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China（中国科学技术大学认知智能国家重点实验室）；Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Harvard University（哈佛大学）；UC Santa Cruz（加州大学圣克鲁兹分校）
Crowdsourcing enables the solicitation of forecasts on a variety of prediction tasks from distributed groups of people. How to aggregate the solicited forecasts, which may vary in quality, into an accurate final prediction remains a challenging yet critical question. Studies have found that weighing expert forecasts more in aggregation can improve the accuracy of the aggregated prediction. However, this approach usually requires access to the historical performance data of the forecasters, which are often not available. In this paper, we study the problem of aggregating forecasts without havin…
TimeSeriesScientist: A General-Purpose AI Agent for Time Series Analysis
Stony Brook University（石溪大学）；University of California, San Diego（加州大学圣地亚哥分校）；University of British Columbia（不列颠哥伦比亚大学）；Zhejiang University（浙江大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Case Western Reserve University（凯斯西储大学）；Fudan University（复旦大学）
MACROCAST: A Vintage-Consistent Time Series Foundation Model for Real-Time Macroeconomic Forecasting
School of Economics and Finance, Queen Mary University of London（伦敦大学玛丽女王学院经济与金融学院）；School of Business and Economics, Brandeis University（布兰迪大学商学院与经济学院）
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
Emory University Department of Computer Science Atlanta GA USA（埃默里大学计算机科学系）；Emory University Rollins School of Public Health Atlanta GA USA（埃默里大学罗林斯公共卫生学院）；Emory University（埃默里大学）
Merging Smarter, Generalizing Better: Enhancing Model Merging on OOD Data
College of Artificial Intelligence, Jilin University（吉林大学人工智能学院）；Rensselaer Polytechnic Institute（拉特盖尔理工学院）；College of applied Science, Taiyuan University of Science and Technology（太原科技大学应用科学学院）；CSIRO’s Data61（CSIRO数据61）
AI-Augmented Predictions: LLM Assistants Improve Human Forecasting Accuracy
London School of Economics and Political Science（伦敦政治经济学院）；Massachusetts Institute of Technology（麻省理工学院）；Federal Reserve Bank of Chicago（芝加哥联邦储备银行）；University of California San Diego（加利福尼亚大学圣迭戈分校）；University of Pennsylvania（宾夕法尼亚大学）
Lancaster University（兰卡斯特大学）；Technical University of Ostrava（俄斯特拉发技术大学）；Sharif University of Technology（谢里夫理工大学）
One of the challenging questions in time series forecasting is how to find the best algorithm. In recent years, a recommender system scheme has been developed for time series analysis using a meta-learning approach. This system selects the best forecasting method with consideration of the time series characteristics. In this paper, we propose a novel approach to focusing on some of the unanswered questions resulting from the use of meta-learning in time series forecasting. Therefore, three main gaps in previous works are addressed including, analyzing various subsets of top forecasters as inpu…
ODTQA-FoRe: An Open-Domain Tabular Question Answering Dataset for Future Data Forecasting and Reasoning
School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Institute of Artificial Intelligence and Future Networks, Beijing Normal University（北京师范大学人工智能与未来网络研究院）；Faculty of Arts and Sciences, Beijing Normal University（北京师范大学文理学院）；Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学）
Accurate and Efficient Multivariate Time Series Forecasting via Offline Clustering
State Key Laboratory of Complex & Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing（未来区块链与隐私计算北京先进创新中心）；Beihang University（北京航空航天大学）；HKGAI（Hong Kong University of Science and Technology）；Research Institute of Trustworthy Autonomous Systems（可信自主系统研究 institute）；Southern University of Science and Technology（南方科技大学）；Department of Data Science（数据科学系）
POCO: Scalable Neural Forecasting through Population Conditioning
EECS, MIT（MIT电子工程与计算机科学系）；SEAS, Harvard University（哈佛大学科学与工程系）；Janelia Research Campus（贾尔尼亚研究校园）；Harvard Medical School（哈佛医学院）；Kempner Institute（凯普纳研究所）；Université de Montréal（蒙特利尔大学）；Mila - Quebec AI Institute（魁北克人工智能研究所）；Stanford University（斯坦福大学）
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）
Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids
Mathematics and Computer Science, Eindhoven University of Technology（埃因霍温理工大学数学与计算机科学学院）；Computer Science, University of Twente（特文特大学计算机科学学院）；Electrical Sustainable Energy, Delft University of Technology（代尔夫特理工大学电力可持续能源学院）；Leiden Institute of Advanced Computer Science, Leiden University（莱顿大学高级计算机科学研究所）；Engineering Technology, University of Twente（特文特大学工程技术学院）；Jheronimus Academy of Data Science, Tilburg University（蒂尔堡大学赫罗尼莫斯数据科学学院）；Alliander N.V.（Alliander公司）；Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学电气工程学院）
FLEXIBLE: Forecasting Cellular Traffic by Leveraging Explicit Inductive Graph-Based Learning
Orange Innovation（奥兰治创新公司）；Nantes University（南特大学）；École Centrale Nantes（南特中央理工学院）；IMT Atlantique（大西洋电信与技术学院）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；LS2N（南特数字科学实验室）；University of Luxembourg（卢森堡大学）；SnT（卢森堡大学安全、可靠性和信任中心）
EMFusion: Uncertainty-Aware Conditional Diffusion Model for Multivariate Narrow-band Exposure Forecasting
department of Electrical Engineering and Computer Science, York University（电气工程与计算机科学系，约克大学）；School of Electrical and Electronic Engineering, Huazhong University of Science and Technology（电子与电气工程学院，华中科技大学）；Central China Branch of State Grid Corporation of China（国家电网公司中部分部）；Department of Electronic Engineering, University of Rome Tor Vergata（罗马大学Tor Vergata电子工程系）；Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)（国家大学间电信研究会（CNIT））
