Forecasting graph-based, time-dependent data has broad practical applications but presents challenges. Effective models must capture both spatial and temporal dependencies in the data, while also incorporating auxiliary information to enhance prediction accuracy. In this paper, we identify limitations in current state-of-the-art models regarding temporal dependency handling. To overcome this, we introduce GSA-Forecaster, a new deep learning model designed for forecasting in graph-based, time-dependent contexts. GSA-Forecaster utilizes graph sequence attention, a new attention mechanism proposed in this paper, to effectively manage temporal dependencies. GSA-Forecaster integrates the data's graph structure directly into its architecture, addressing spatial dependencies. Additionally, it incorporates auxiliary information to refine its predictions further. We validate its performance using real-world graph-based, time-dependent datasets, where it demonstrates superior effectiveness compared to existing state-of-the-art models.
Relative Geometry of Neural Forecasters: Linking Accuracy and Alignment in Learned Latent Geometry
Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；School of Embedded Composite Artificial Intelligence (SECAI)（嵌入式复合人工智能学院）；Leipzig University（莱比锡大学）；Max Planck Institute for Mathematics in the Sciences（马克斯·普朗克数学研究院）
ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities
Forecasting Research Institute（预测研究所）；Federal Reserve Bank of Chicago（芝加哥联邦储备银行）；New York University（纽约大学）；University of California, Berkeley（加州大学伯克利分校）；University of Pennsylvania（宾夕法尼亚大学）
Understanding Key Features of Time Series Foundation Models from Epidemic Forecasting
Department of Computer Science, School of Engineering and Applied Science, University of Virginia（弗吉尼亚大学工程与应用科学学院计算机科学系）；School of Data Science, University of Virginia（弗吉尼亚大学数据科学学院）；Biocomplexity Institute, University of Virginia（弗吉尼亚大学生物复杂性研究所）；Department of Electrical and Computer Engineering, School of Engineering and Applied Science, University of Virginia（弗吉尼亚大学工程与应用科学学院电气与计算机工程系）
SVTime: Small Time Series Forecasting Models Informed by "Physics" of Large Vision Model Forecasters
University of Houston（德克萨斯大学休斯顿分校）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Connecticut（康涅狄格大学）；Florida International University（佛罗里达国际大学）；Squirrel Ai Learning（squirrel ai 学习）
vLinear: A Powerful Linear Model for Multivariate Time Series Forecasting
State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University（人工智能通用基础理论国家重点实验室，智能科学与技术学院，北京大学）
ROAR: Retrieval Opportunity-Aware Refinement for Zero-Shot Time Series Forecasting
College of Computer Science and Technology, Jilin University（吉林大学计算机科学与技术学院）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算机与数据科学学院）；City St George’s, University of London（伦敦大学城圣乔治学院）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）
TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecaster
School of Computing, University of Connecticut（康奈尔大学计算机学院）；Department of Machine Learning Research, Morgan Stanley（摩根士丹利机器学习研究部）；Ant Group（蚂蚁集团）；TWG Global（TWG全球）
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）
MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters
University of Houston（休斯顿大学）；NEC Labs（NEC实验室）；University of Waterloo（滑铁卢大学）；University of Connecticut（康涅狄格大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Singapore Management University（新加坡管理大学）
HTMformer: Hybrid Time and Multivariate Transformer for Time Series Forecasting
Northwestern Polytechnical University（北华理工大学）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（人工智能教育部重点实验室，上海交通大学AI研究院）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（宁波数字孪生研究所，东部技术研究所）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative（宁波空间智能与数字衍生关键实验室）
Community HydroAgent: Formalizing Forecaster Expertise into Skill-Orchestrated Flood Forecasting Workflows
Politecnico di Milano（米兰理工大学）；China Three Gorges University（三峡大学）；Delft University of Technology（代尔夫特理工大学）；Qinhuangdao Hydrological Survey and Research Center of Hebei Province（河北省秦皇岛水文勘测研究中心）；Dalian University of Technology（大连理工大学）；TU Dresden University of Technology（德累斯顿工业大学）；Ningbo University（宁波大学）；China IPPR International Engineering Co., Ltd.（中国市政工程华北设计研究总院有限公司）；Hohai University（河海大学）；Nanjing University（南京大学）；Cornell University（康奈尔大学）；Tongji University（同济大学）；Zhejiang Institute of Hydraulics and Estuary（浙江省水利河口研究院）；Ghent University（根特大学）；University of British Columbia（不列颠哥伦比亚大学）；Beijing Normal University（北京师范大学）；Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工大学）
TIMBRE: Teaching Time Series Forecasters to Read, Remember, and Reconcile
Alibaba Group（阿里巴巴集团）；Xiaomi（小米）；University of Electronic Science and Technology of China（电子科技大学）；University of Bristol（布里斯托大学）；University of California, Irvine（加州大学尔湾分校）；University of Pennsylvania（宾夕法尼亚大学）
