Continuous-time event sequences play a vital role in real-world domains such as healthcare, finance, online shopping, social networks, and so on. To model such data, temporal point processes (TPPs) have emerged as the most natural and competitive models, making a significant impact in both academic and application communities. Despite the emergence of many powerful models in recent years, there hasn't been a central benchmark for these models and future research endeavors. This lack of standardization impedes researchers and practitioners from comparing methods and reproducing results, potentially slowing down progress in this field. In this paper, we present EasyTPP, the first central repository of research assets (e.g., data, models, evaluation programs, documentations) in the area of event sequence modeling. Our EasyTPP makes several unique contributions to this area: a unified interface of using existing datasets and adding new datasets; a wide range of evaluation programs that are easy to use and extend as well as facilitate reproducible research; implementations of popular neural TPPs, together with a rich library of modules by composing which one could quickly build complex models. All the data and implementation can be found at https://github.com/ant-research/EasyTemporalPointProcess. We will actively maintain this benchmark and welcome contributions from other researchers and practitioners. Our benchmark will help promote reproducible research in this field, thus accelerating research progress as well as making more significant real-world impacts.
A Survey and Benchmarking of Spatial-Temporal Traffic Data Imputation Models
Key Laboratory of Big Data and Artificial Intelligence in Transportation, Ministry of Education, and School of Computer Science and Technology, Beijing Jiaotong University（大数据与人工智能交通运输关键实验室、教育部，计算机科学与技术学院，北京交通大学）；Department of Computer Science, Aalborg University（计算机科学系，奥尔堡大学）；Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence, School of Computer Science and Technology, Beijing Jiaotong University（北京交通大数据挖掘与具身智能关键实验室，计算机科学与技术学院，北京交通大学）
Benchmarking Machine Learning Architectures for Antimicrobial Stewardship in Pediatric ICUs
Department of Intensive Care and Neonatology and Children’s Research Center University of Zurich University Children’s Hospital Zurich（重症护理与新生儿科及儿童研究中心，苏黎世大学苏黎世儿童医院）；Department of Health Sciences and Technology ETH Zurich（健康科学与技术系，苏黎世联邦理工学院）；Department of Computer Science ETH Zurich（计算机科学系，苏黎世联邦理工学院）
PredBench: Benchmarking Spatio-Temporal Prediction across Diverse Disciplines
Shanghai AI Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Shanghai Jiao Tong University（上海交通大学）；Sydney University（悉尼大学）；The University of Hong Kong（香港大学）
TemporalBench: Benchmarking Fine-grained Temporal Understanding for Multimodal Video Models
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Microsoft Research（微软研究院）；Ohio State University（俄亥俄州立大学）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；Northeastern University（东北大学）；University of California, Santa Cruz（加利福尼亚大学圣克鲁兹分校）；Chinese University of Hong Kong（香港中文大学）；Illinois Institute of Technology（伊利诺伊理工学院）；Georgia Institute of Technology（佐治亚理工学院）
提出 TemporalBench 基准，通过约 10K 高质量人工标注视频问答对评估多模态视频模型的细粒度时序理解能力，发现 GPT-4o 准确率仅 38.5%，并提出 MBA 指标纠正多选问答偏差。
XFinBench: Benchmarking LLMs in Complex Financial Problem Solving and Reasoning
School of Computing and Information Systems, Singapore Management University（计算与信息系统学院，新加坡管理大学）；Institute of Trustworthy Embodied AI, Fudan University（可信具身人工智能研究院，复旦大学）
ChaosNetBench: Benchmarking Spatio-Temporal Graph Neural Networks on Chaotic Lattice Dynamics
Centre for Artificial Intelligence Research (CAIR)（人工智能研究中心（CAIR））；University of Cape Town（开普敦大学）；Nonlinear Dynamics and Chaos Group（非线性动力学与混沌组）；Department of Mathematics and Applied Mathematics（数学与应用数学系）
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR China（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle, Newcastle, Australia（新castle大学计算机与信息科学学院）；Hangzhou Innovation Institute of Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
