Recent progress in neural forecasting accelerated improvements in the performance of large-scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two common challenges afflicting the task are the volatility of the predictions and their computational complexity. We introduce N-HiTS, a model which addresses both challenges by incorporating novel hierarchical interpolation and multi-rate data sampling techniques. These techniques enable the proposed method to assemble its predictions sequentially, emphasizing components with different frequencies and scales while decomposing the input signal and synthesizing the forecast. We prove that the hierarchical interpolation technique can efficiently approximate arbitrarily long horizons in the presence of smoothness. Additionally, we conduct extensive large-scale dataset experiments from the long-horizon forecasting literature, demonstrating the advantages of our method over the state-of-the-art methods, where N-HiTS provides an average accuracy improvement of almost 20% over the latest Transformer architectures while reducing the computation time by an order of magnitude (50 times). Our code is available at bit.ly/3VA5DoT
Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecasting
Faculty of Electrical Engineering and Computing, University of Zagreb（Zagreb大学电气工程与计算学院）；It From Bit d.o.o.（It From Bit公司）；Babson College（巴布森学院）；mStart Plus d.o.o.（mStart Plus公司）
GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
University of Chinese Academy of Sciences（中国科学院大学）；School of Computer Science and Technology, Huazhong University of Science and Technology（华中科技大学计算机科学与技术学院）
From ARIMA to Attention: Power Load Forecasting Using Temporal Deep Learning
Department of Computer Science and Engineering（计算机科学与工程系）；Vellore Institute of Technology（韦洛雷理工学院）；Department of Electronics and Communication Engineering（电子与通信工程系）；Bharat Institute of Engineering and Technology（巴拉特工程与技术学院）
Probabilistic Forecasting for Dynamical Systems with Missing or Imperfect Data
Institute of Applied Mathematics（应用数学研究所）；University of British Columbia（不列颠哥伦比亚大学）；Recherche en prévision numérique atmosphérique（大气数值预报研究中心）；Environnement et Changement climatique Canada（加拿大环境与气候变化部）
FuXi-Air: Urban Air Quality Forecasting Based on Emission-Meteorology-Pollutant multimodal Machine Learning
Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention（上海大气颗粒污染与防治重点实验室）；National Observations and Research Station for Wetland Ecosystems of the Yangtze Estuary（长江入海口湿地生态系统观测与研究站）；Department of Environmental Science and Engineering（环境科学与工程学院）；Shanghai Academy of AI for Science (SAIS)（上海人工智能科学研究院）；MOE Laboratory for National Development and Intelligent Governance（教育部国家发展与智能治理实验室）；Shanghai Institute for Energy and Carbon Neutrality Strategy（上海能源与碳中和战略研究院）；IRDR ICoE on Risk Interconnectivity and Governance on Weather/Climate Extremes Impact and Public Health（国际减灾与发展研究院风险互联与治理国际联合实验室）；Shanghai Institute of Eco-Chongming (SIEC)（上海生态崇明研究院）；Shanghai Environmental Monitoring Center (SEMC)（上海市环境监测中心）
Trojan Horse Hunt in Time Series Forecasting for Space Operations
KP Labs（KP实验室）；Silesian University of Technology（桑特大学）；Warsaw University of Technology（华沙技术大学）；European Space Agency（欧洲航天局）；European Space Operations Center（欧洲空间运营中心）
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
Motherbrain, EQT Group（Motherbrain、EQT集团）；KTH Royal Institute of Technology（皇家理工学院）；Mitsubishi UFJ Trust Investment Technology Institute（三菱UFJ信托投资技术研究所）；Technical University of Denmark（丹麦技术大学）
Università degli Studi di Napoli Federico II（那不勒斯费德里科二世大学）；Technion — Israel Institute of Technology（以色列理工学院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Scuola Superiore Meridionale（南方高等学校）
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
School of Transportation Engineering, East China Jiao Tong University（东华交通大学交通运输工程学院）；Jiangxi Provincial Key Laboratory of Water Ecological Conservation in Headwater Regions（江西省头水地区水生态保育重点实验室）；Division of Geodetic Science, School of Earth Sciences, The Ohio State University（地球科学学院地质科学系，俄亥俄州立大学）；Institute Dom Luiz, University of Beira Interior（贝拉内里亚大学多姆路易斯研究所）；Institute of Meteorology and Climatology, Leibniz University Hannover（汉诺威莱比锡大学气象与气候研究所）；Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University（江西省鄱阳湖湿地与流域研究重点实验室，江西省教育厅，江西师范大学）
Physics-Informed Singular-Value Learning for Cross-Covariances Forecasting in Financial Markets
Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes（巴黎-萨克雷大学，CentraleSupélec，复杂性与系统数学与计算机科学）；Università degli Studi di Palermo, Dipartimento di Fisica e Chimica "Emilio Segrè"（巴勒莫大学，Emilio Segrè物理与化学系）
Learning to Advect: A Neural Semi-Lagrangian Architecture for Weather Forecasting
Recherche en prévision numérique atmosphérique, Environnement et Changement climatique Canada（环境与气候变化加拿大大气数值预报研究）；Department of Earth, Ocean and Atmospheric Sciences, University of British Columbia（不列颠哥伦比亚大学地球、海洋和大气科学系）；Department of Computer Science, University of Saskatchewan（萨斯喀彻温大学计算机科学系）；Department of Mechanical Engineering, Rochester Institute of Technology（罗切斯特理工学院机械工程系）
Deep Learning vs. Statistical Models for Multi-Horizon Price Forecasting of Second-Hand Electronics: A Systematic Benchmark
Faculty of Economic Sciences, University of Warsaw（华沙大学经济科学学院）；WeSub；Faculty of Mathematics and Information Science, Warsaw University of Technology（华沙理工大学数学与信息科学学院）
Neural Fields for Fast and Scalable Interpolation of Geophysical Ocean Variables
Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；CNRS（法国国家科学研究中心）；Institut Pierre Simon Laplace（皮埃尔·西蒙·拉普拉斯研究所）；IRD（法国发展研究院）；Sorbonne Université（索邦大学）；IMT Atlantique（大西洋国立高等工业技术学院）
Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings
Yardi School of Artificial Intelligence (ScAI), Indian Institute of Technology Delhi（印度理工学院德里分校亚迪人工智能学院）；Department of Neurology, Vardhman Mahavir Medical College and Safdarjung Hospital（瓦尔丹·马哈维尔医学院和萨夫达戎医院神经内科）；Department of Applied Mechanics, Indian Institute of Technology Delhi（印度理工学院德里分校应用力学系）
Neural Fourier Modelling: A Highly Compact Approach to Time-Series Analysis
The University of Auckland（奥克兰大学）；School of Computer Science（计算机科学学院）；Acoustics Vibration Research Centre（声学与振动研究中心）；Mechanical Mechatronics Engineering（机械与机电工程学院）；Strong AI Lab（强人工智能实验室）
