The surging demand for cloud computing resources, driven by the rapid growth of sophisticated large-scale models and data centers, underscores the critical importance of efficient and adaptive resource allocation. As major tech enterprises deploy massive infrastructures with thousands of GPUs, existing cloud platforms still struggle with low resource utilization due to key challenges: capturing hierarchical indicator structures, modeling non-Gaussian distributions, and decision-making under uncertainty. To address these challenges, we propose HRAMONY, an adaptive Hierarchical Attention-based Resource Modeling and Decision-Making System. HARMONY combines hierarchical multi-indicator distribution forecasting and uncertainty-aware Bayesian decision-making. It introduces a novel hierarchical attention mechanism that comprehensively models complex inter-indicator dependencies, enabling accurate predictions that can adapt to evolving environment states. By transforming Gaussian projections into adaptive non-Gaussian distributions via Normalizing Flows. Crucially, HARMONY leverages the full predictive distributions in an adaptive Bayesian process, proactively incorporating uncertainties to optimize resource allocation while robustly meeting SLA constraints under varying conditions. Extensive evaluations across four large-scale cloud datasets demonstrate HARMONY's state-of-the-art performance, significantly outperforming nine established methods. A month-long real-world deployment validated HARMONY's substantial practical impact, realizing over 35,000 GPU hours in savings and translating to $100K+ in cost reduction, showcasing its remarkable economic value through adaptive, uncertainty-aware scaling. Our code is available at https://github.com/Floating-LY/HARMONY1.
HieraMix: A Hierarchical MLP-Mixer for Large-Scale Traffic Forecasting
State Key Laboratory of Complex & Critical Software Environment (SKLCCSE)（复杂关键软件环境国家重点实验室）；School of Computer Science and Engineering, Beihang University（北京航空航天大学计算机科学与工程学院）；MOE Engineering Research Center of Advanced Computer Application Technology, Beihang University（教育部先进计算机应用技术工程研究中心，北京航空航天大学）
Short-Term Electricity Demand Forecasting for New England: A Comprehensive Machine Learning Benchmark with Weather, Calendar, and COVID-19 Indicators
Department of Chemical and Materials Engineering, New Mexico State University（新墨西哥州立大学化学与材料工程系）；Department of Electrical and Communications Engineering, New Jersey Institute of Technology（新泽西理工学院电气与通信工程系）
EpiFlow: A framework for improving the utility of wastewater signals for disease forecasting
Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）；Virginia Department of Health（弗吉尼亚州卫生部门）；US Department of Defense, Pentagon（美国国防部五角大楼）；Old Dominion University（奥多明尼昂大学）
Cryptocurrency Price Forecasting Using XGBoost Regressor and Technical Indicators
ESISA Analytica, Higher School of Engineering in Applied Sciences（ESISA分析与应用科学高等工程学院）；GAIA, Ericsson（GAIA、爱立信）；Fordham University（福特汉姆大学）；Penn State University（宾夕法尼亚州立大学）
HighAir: A Hierarchical Graph Neural Network-Based Air Quality Forecasting Method
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；College of Computer Science, Zhejiang University of Technology（浙江工业大学计算机科学学院）；Alibaba Group（阿里巴巴集团）
HUTFormer: Hierarchical U-Net Transformer for Long-Term Traffic Forecasting
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Computer Science and Technology, University of Chinese Academy of Sciences（中国科学院大学计算机科学与技术学院）；School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Department of Planning, Design, and Technology of Architecture, Sapienza University of Rome（罗马大学建筑规划、设计与技术系）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与移动性学院）
Learning the Dynamic Correlations and Mitigating Noise by Hierarchical Convolution for Long-term Sequence Forecasting
Peking University（北京大学）；Key Laboratory of High Confidence Software Technologies, Ministry of Education（高可信软件技术教育部重点实验室）；National Engineering Research Center of Software Engineering, Peking University（北京大学软件工程国家工程研究中心）
HDT: Hierarchical Discrete Transformer for Multivariate Time Series Forecasting
College of Computing and Data Science, Nanyang Technological University (NTU)（南洋理工大学计算与数据科学学院）；Webank-NTU Joint Research Institute on Fintech, NTU（微众银行-南洋理工大学金融科技联合研究院）；Tencent AI Lab（腾讯人工智能实验室）
