In various applications, probabilistic forecasts are required to quantify the inherent uncertainty associated with the forecast. However, numerous modern forecasting methods are still designed to create deterministic forecasts. Transforming these deterministic forecasts into probabilistic forecasts is often challenging and based on numerous assumptions that may not hold in real-world situations. Therefore, the present article proposes a novel approach for creating probabilistic forecasts from arbitrary deterministic forecasts. In order to implement this approach, we use a conditional Invertible Neural Network (cINN). More specifically, we apply a cINN to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast to generate accurate probabilistic forecasts. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions. Besides showing the mathematical validity of our approach, we empirically show that our approach noticeably outperforms traditional methods for including uncertainty in deterministic forecasts and generally outperforms state-of-the-art probabilistic forecasting benchmarks.
IN-Flow: Instance Normalization Flow for Non-stationary Time Series Forecasting
University of Oxford（牛津大学）；Microsoft Research Asia（微软亚洲研究院）；University of Macau（澳门大学）；University of Central Florida（中佛罗里达大学）；State Information Center（国家信息中心）；Tongji University（同济大学）；Arizona State University（亚利桑那州立大学）
A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting
School of Engineering（工程学院）；Brown University（布朗大学）；Department of Epidemiology（流行病学系）；School of Public Health（公共卫生学院）；Institute at Brown for Environment and Society（布朗大学环境与社会研究所）
TimeSynth: A Framework for Uncovering Systematic Biases in Time Series Forecasting
Scientific Computing and Imaging Institute, University of Utah（科学计算与成像研究所，犹他大学）；Kahlert School of Computing, University of Utah（Kahlert计算学院，犹他大学）；Mountain Biometrics Inc.（山景生物医学公司）；Department of Psychiatry, University of Utah（精神病学系，犹他大学）
UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting
National University of Singapore（新加坡国立大学）；The Hong Kong University of Science and Technology（香港科技大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Enhancing Algorithm Performance Understanding through tsMorph: Generating Semi-Synthetic Time Series for Robust Forecasting Evaluation
LIACC/Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院/LIACC研究所）；Institute of Mathematical and Computer Sciences, University of São Paulo（圣保罗大学数学与计算机科学研究所）；Fraunhofer AICOS Portugal（弗劳恩霍夫葡萄牙AICOS研究所）
Time series forecasting for multidimensional telemetry data using GAN and BiLSTM in a Digital Twin
COPPE, Universidade Federal do Rio de Janeiro（里约热内卢联邦大学COPPE）；UFF, Universidade Federal Fluminense（弗卢米嫩塞联邦大学）；UNESP, Universidade Estadual Paulista（圣保罗州立大学）
Rethinking Recurrent Neural Networks for Time Series Forecasting: A Reinforced Recurrent Encoder with Prediction-Oriented Proximal Policy Optimization
School of Management, Huazhong University of Science and Technology（华中科技大学管理学院）；Center for Applied Mathematics, École des Ponts ParisTech（巴黎高等理工学院应用数学中心）；School of Mechanical Engineering, Dalian Jiaotong University（大连交通大学机械工程学院）；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）
What is the best RNN-cell structure to forecast each time series behavior?
University of Granada（格拉纳达大学）；ENSIAS, Mohammed V University of Rabat（拉巴特穆罕默德五世大学ENSIAS学院）；Andalusian Research Institute in Data Science and Computational Intelligence, DaSCI（安达卢西亚数据科学与计算智能研究院）
Medium-Term Multi-Resolution Electric Load Forecasting using Economic Data and Foundation Model
Atos（源讯公司）；EDF（法国电力公司）；Laboratoire des Sciences du Climat & de l’Environnement（气候与环境科学实验室）；CEA（法国原子能和替代能源委员会）；CNRS（法国国家科学研究中心）；UVSQ（凡尔赛大学）；Université Paris-Saclay（巴黎萨克雷大学）；Laboratoire de Mathématiques d’Orsay（奥赛数学实验室）
Cold-Start Forecasting of New Product Life-Cycles via Conditional Diffusion Models
Guanghua School of Management, Peking University（北京大学光华管理学院）；Department of Industrial Engineering and Decision Analytics, The Hong Kong University of Science and Technology（香港科技大学工业工程与决策分析系）
Online Data Augmentation for Forecasting with Deep Learning
Faculdade de Engenharia da Universidade do Porto（波尔图大学工程学院）；Laboratory for Artificial Intelligence and Computer Science (LIACC)（人工智能与计算机科学实验室）；INESC TEC；Fraunhofer Portugal AICOS（弗劳恩霍夫葡萄牙AICOS）
