Exogenous variables offer valuable supplementary information for predicting future endogenous variables. Forecasting with exogenous variables needs to consider both past-to-future dependencies (i.e., temporal correlations) and the influence of exogenous variables on endogenous variables (i.e., channel correlations). This is pivotal when future exogenous variables are available, because they may directly affect the future endogenous variables. Many methods have been proposed for time series forecasting with exogenous variables, focusing on modeling temporal and channel correlations. However, most of them use a two-step strategy, modeling temporal and channel correlations separately, which limits their ability to capture joint correlations across time and channels. Furthermore, in real-world scenarios, time series are frequently affected by various forms of noises, underscoring the critical importance of robustness in such correlations modeling. To address these limitations, we propose GCGNet, a Graph-Consistent Generative Network for time series forecasting with exogenous variables. Specifically, GCGNet first employs a Variational Generator to produce coarse predictions. A Graph Structure Aligner then further guides it by evaluating the consistency between the generated and true correlations, where the correlations are represented as graphs, and are robust to noises. Finally, a Graph Refiner is proposed to refine the predictions to prevent degeneration and improve accuracy. Extensive experiments on 12 real-world datasets demonstrate that GCGNet outperforms state-of-the-art baselines.
Leveraging Exogenous Signals for Hydrology Time Series Forecasting
College of Computing（计算学院）；Georgia Institute of Technology（佐治亚理工学院）；University of Virginia（弗吉尼亚大学）；School of Data Science（数据科学学院）；Computer Science（计算机科学）；Biocomplexity Institute（生物复杂性研究所）
Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
Artificial Intelligence Department, RobotBulls Labs（人工智能部门，RobotBulls实验室）；Department of Electrical and Computer Engineering, North South University（电气与计算机工程系，北南大学）
SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting
Shanghai Advanced Research Institute, Chinese Academy of Sciences（中国科学院上海先进研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Physical Science and Technology, Shanghaitech University（上海科技大学物理科学与技术学院）；State Power Investment Corporation Limited（国家电力投资集团有限公司）；China University of Petroleum（中国石油大学）
Forecasting Brazilian and American COVID-19 cases based on artificial intelligence coupled with climatic exogenous variables
Pontifical Catholic University of Parana (PUCPR)（巴拉那天主教大学）；Federal Technological University of Parana (UTFPR)（巴拉那联邦理工大学）；Federal University of Parana (UFPR)（巴拉那联邦大学）
Aura: Universal Multi-dimensional Exogenous Integration for Aviation Time Series
School of Software, BNRist, Tsinghua University（软件学院、BNRist、清华大学）；Hongshen College, Chongqing University（弘深学院、重庆大学）；School of Software, Tsinghua University（软件学院、清华大学）；China Southern Airlines（中国南方航空）
Time Distributed Deep Learning Models for Purely Exogenous Forecasting: Application to Water Table Depth Prediction using Weather Image Time Series
Computer Science Department - University of Turin（都灵大学计算机科学系）；LISTIC Laboratory - Université Savoie Mont Blanc（萨瓦伊-蒙布莱大学LISTIC实验室）；Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino and University of Turin（都灵理工学院和都灵大学区域与城市研究与规划联合学院）
Experimental study of time series forecasting methods for groundwater level prediction
University Clermont Auvergne（克莱蒙奥弗涅大学）；Clermont Auvergne INP（克莱蒙奥弗涅国立理工学院）；CNRS（法国国家科学研究中心）；Mines Saint-Etienne（圣艾蒂安国立高等矿业学校）；Inria（法国国家信息与自动化研究所）；Université de Tours（图尔大学）；BRGM（法国地质调查局）
XRMDN: An Extended Recurrent Mixture Density Network for Short-Term Probabilistic Rider Demand Forecasting with High Volatility
Concordia University（康考迪亚大学）；Concordia Institute for Information Systems Engineering (CIISE)（康考迪亚信息系统工程研究所）；Concordia John Molson School of Business (JMSB)（康考迪亚约翰莫尔森商学院）
针对 MoD 需求受内外因素影响且高波动、传统点预测难以刻画不确定性的问题，提出 XRMDN 深度概率预测框架，并在真实数据集上验证其优于基准模型。
CTIA-ODE: Continuous-Time Inter-Variable Attention for Long-Term Time Series Forecasting
UGOD, HKUST-GZ（香港科技大学（广州））；School of Software, Yunnan University（云南大学软件学院）；UGOD, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
2DXformer: Dual Transformers for Wind Power Forecasting with Dual Exogenous Variables
School of Artificial Intelligence, Hebei University of Technology, Tianjin, China（人工智能学院，河北工业大学，天津，中国）；State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China（电力设备可靠性与智能化国家重点实验室，河北工业大学，天津，中国）；Hebei Province Key Laboratory of Big Data Calculation, Hebei University of Technology, Tianjin, China（河北省大数据计算重点实验室，河北工业大学，天津，中国）
Robust Short-Term OEE Forecasting in Industry 4.0 via Topological Data Analysis
Institute of Applied Mathematics, Middle East Technical University（应用数学研究所，中东部技术大学）；Network Technologies Department, TÜBİTAK ULAKBİM（网络技术部门，TÜBİTAK ULAKBİM）；Department of Statistics, Middle East Technical University（统计学系，中东部技术大学）
Forecasting Conceptual Diffusion in Science: The Case of Quantum Computing
Geneva School of Economics and Management, University of Geneva（日内瓦经济管理学院，日内瓦大学）；Faculty of Medicine, University of Geneva（日内瓦大学医学院）；Open Quantum Institute, CERN（开放量子研究所，欧洲核子研究中心）；armasuisse Science + Technology（armasuisse 科学与技术）
