The complex spatial-temporal correlations in transportation networks make the traffic forecasting problem challenging. Since transportation system inherently possesses graph structures, many research efforts have been put with graph neural networks. Recently, constructing adaptive graphs to the data has shown promising results over the models relying on a single static graph structure. However, the graph adaptations are applied during the training phases and do not reflect the data used during the testing phases. Such shortcomings can be problematic especially in traffic forecasting since the traffic data often suffer from unexpected changes and irregularities in the time series. In this study, we propose a novel traffic forecasting framework called Progressive Graph Convolutional Network (PGCN). PGCN constructs a set of graphs by progressively adapting to online input data during the training and testing phases. Specifically, we implemented the model to construct progressive adjacency matrices by learning trend similarities among graph nodes. Then, the model is combined with the dilated causal convolution and gated activation unit to extract temporal features. With residual and skip connections, PGCN performs the traffic prediction. When applied to seven real-world traffic datasets of diverse geometric nature, the proposed model achieves state-of-the-art performance with consistency in all datasets. We conclude that the ability of PGCN to progressively adapt to input data enables the model to generalize in different study sites with robustness.
Graph-based Forecasting with Missing Data through Spatiotemporal Downsampling
IDSIA USI-SUPSI（IDSIA USI-SUPSI（瑞士意大利语大学与瑞士应用科学大学联合机构））；Università della Svizzera italiana（瑞士意大利语大学）；Politecnico di Milano（米兰理工大学）；UiT the Arctic University of Norway（挪威北极大学）；NORCE, Norwegian Research Centre AS（NORCE挪威研究中心）
Amortized Predictability-aware Training Framework for Time Series Forecasting and Classification
Shanghai Key Laboratory of Data Science, College of Computer Science and Artificial Intelligence Fudan University（复旦大学计算机科学与人工智能学院上海数据科学关键实验室）；Department of Electrical and Computer Engineering University of British Columbia (UBC)（英属哥伦比亚大学电气与计算机工程系）
CycloneMAE: A Scalable Multi-Task Learning Model for Global Tropical Cyclone Probabilistic Forecasting
School of Computer Science, School of Software（计算机科学学院、软件学院）；Nanjing University of Information Science and Technology（南京信息工程大学）；School of Atmospheric Sciences（大气科学学院）；Shanghai Typhoon Institute（上海台风研究所）；China Meteorological Administration（中国气象局）；Nanjing University of Posts and Telecommunications（南京邮电大学）
Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System
Stanford University（斯坦福大学）；University of Technology Sydney（悉尼科技大学）；CSIRO’s Data61（澳大利亚联邦科学与工业研究组织Data61）；University of New South Wales（新南威尔士大学）；University of Science and Technology of China（中国科学技术大学）
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
School of Software Engineering Sun Yat-Sen University（软件工程学院中山大学）；Institute of Data Science（数据科学研究院）；School of Computing（计算机学院）；National University of Singapore（新加坡国立大学）
DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting
School of Computer Science and Technology（计算机科学与技术学院）；Xi'an Jiaotong University（西安交通大学）；Department of Computer Science and Technology（计算机科学与技术系）；Tsinghua University（清华大学）
Structured Latent Space Modeling over Multi-Scale Temporal Patches for Multivariate Time Series Forecasting
School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）；Innovation Engineering College, Macau University of Science and Technology（澳门科技大学创新工程学院）
TianQuan-S2S: A Subseasonal-to-Seasonal Global Weather Model via Incorporate Climatology State
Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）；National Supercomputing Center in Shenzhen（深圳国家超算中心）；Huawei Technologies Co Ltd（华为技术有限公司）
Initialization Is Critical: Advancing Federated Short-Term Load Forecasting under Load Heterogeneity via Model Initialization
School of Electrical Engineering and Computer Science, The Pennsylvania State University（宾夕法尼亚州立大学电气工程与计算机科学学院）；Department of Electrical and Computer Engineering, Bucknell University（巴克内尔大学电气与计算机工程系）
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）
