Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings
Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynamic, multi-modal approach that integrates the strengths of traditional forecasting methods and instruction tuning of small language models for time series trend analysis. This approach utilizes a mixture of experts (MoE) architecture with parameter-efficient fine-tuning (PEFT) methods, tailored for consumer hardware to scale up AI solutions in low resource settings while balancing performance and latency tradeoffs. Additionally, our approach leverages related past experiences for similar input time series to efficiently handle both intra-series and inter-series dependencies of non-stationary data with a time-then-space modeling approach, using grouped-query attention, while mitigating the limitations of traditional forecasting techniques in handling distributional shifts. Our approach models predictive uncertainty to improve decision-making. Our framework enables on-premises customization with reduced computational and memory demands, while maintaining inference speed and data privacy/security. Extensive experiments on various real-world datasets demonstrate that our framework provides robust and accurate forecasts, significantly outperforming existing methods.
Advancing Spatiotemporal Prediction using Artificial Intelligence: Extending the Framework of Geographically and Temporally Weighted Neural Network (GTWNN) for Differing Geographical and Temporal Contexts
University of New South Wales（新南威尔士大学）；City Futures Research Centre（城市未来研究中心）
University of Pittsburgh（匹兹堡大学）；University of Minnesota（明尼苏达大学）；University of Maryland（马里兰大学）
Spatio-temporal machine learning is critically needed for a variety of societal applications, such as agricultural monitoring, hydrological forecast, and traffic management. These applications greatly rely on regional features that characterize spatial and temporal differences. However, spatio-temporal data often exhibit complex patterns and significant data variability across different locations. The labels in many real-world applications can also be limited, which makes it difficult to separately train independent models for different locations. Although meta learning has shown promise in mo…
Reprogramming Foundational Large Language Models(LLMs) for Enterprise Adoption for Spatio-Temporal Forecasting Applications: Unveiling a New Era in Copilot-Guided Cross-Modal Time Series Representation Learning
The evolution of AI is advancing rapidly, creating both challenges and opportunities for industry-community collaboration. In this work, we present a novel methodology aiming to facilitate this collaboration through crowdsourcing of AI models. Concretely, we have implemented a system and a process that any organization can easily adopt to host AI competitions. The system allows them to automatically harvest and evaluate the submitted models against in-house proprietary data and also to incorporate them as reusable services in a product.
MicroCrackAttentionNeXt: Advancing Microcrack Detection in Wave Field Analysis Using Deep Neural Networks through Feature Visualization
Christian Albrechts University（基尔基督教-阿尔布雷希茨大学）；Aligarh Muslim University（阿里格尔穆斯林大学）；Texas A&M University（德克萨斯农工大学）；Institute of Computer Science（计算机科学研究所）
CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy
Department of Electronic Engineering, Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院电子工程系）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Shanghai AI Laboratory（上海人工智能实验室）；Department of Cardiology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua Medicine, Tsinghua University（清华大学临床医学院清华大学医学部北京清华长庚医院心内科）；Department of Anesthesiology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院国家癌症中心国家肿瘤临床医学研究中心肿瘤医院麻醉科）；Department of Electrocardiographic Diagnosis, Fuzhou University Affiliated Provincial Hospital（福州大学附属省立医院心电图诊断科）
Despite the rapid progress of neuromorphic computing, the inadequate depth and the resulting insufficient representation power of spiking neural networks (SNNs) severely restrict their application scope in practice. Residual learning and shortcuts have been evidenced as an important approach for training deep neural networks, but rarely did previous work assess their applicability to the characteristics of spike-based communication and spatiotemporal dynamics. This negligence leads to impeded information flow and the accompanying degradation problem. In this paper, we identify the crux and the…
The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳－香港产学研协同创新研究院）；College of Mathematics and Statistics, Shenzhen University（深圳大学数学与统计学院）；Shenzhen Youjia Innov Tech Co., Ltd（深圳优家创新科技有限公司）
DeepMind；Waymo；Google（谷歌）；Amazon（亚马逊）；Facebook AI；Sea AI Lab
Travel-time prediction constitutes a task of high importance in transportation networks, with web mapping services like Google Maps regularly serving vast quantities of travel time queries from users and enterprises alike. Further, such a task requires accounting for complex spatiotemporal interactions (modelling both the topological properties of the road network and anticipating events -- such as rush hours -- that may occur in the future). Hence, it is an ideal target for graph representation learning at scale. Here we present a graph neural network estimator for estimated time of arrival (…
School of Electronic Information and Electrical Engineering, Dalian University of Technology（大连理工大学电子信息与电气工程学院）；Peng Cheng Laboratory（鹏城实验室）
Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, especially deep learning method, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey on deep learning-based approaches in tr…
DeepMIDE: A Multi-Output Spatio-Temporal Method for Ultra-Scale Offshore Wind Energy Forecasting
Department of Industrial Engineering, Clemson University（克莱姆森大学工业工程系）；Department of Computer Science, Rutgers University（罗杰斯大学计算机科学系）；Department of Statistics, Rutgers University（罗杰斯大学统计学系）；Department of Industrial & Systems Engineering, Rutgers University（罗杰斯大学工业与系统工程系）；University College, Korea University（韩国大学大学学院）
Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
National Institute of Informatics（国立情报学研究所）；Yokohama National University（横滨国立大学）；University of Southern California（南加州大学）；Grenoble-INP, Ensimag（格勒诺布尔综合理工学院，恩西马格）；Université Clermont Auvergne, ISIMA（克莱蒙奥弗涅大学，ISIMA）；Google DeepMind（谷歌DeepMind）
CINeMA: Conditional Implicit Neural Multi-Modal Atlas for a Spatio-Temporal Representation of the Perinatal Brain
School of Computation, Information and Technology, and the School of Medicine and Health（计算信息学院及医学健康学院）；Technical University of Munich（慕尼黑技术大学）；Department of Computing（计算系）；Institut de Neurosciences de la Timone, UMR 7289, CNRS, Aix-Marseille Université（神经科学研究所，UMR 7289，CNRS，艾克斯-马赛大学）；Aix-Marseille Univ, APHM, Service de Neuroradiologie Diagnostique et Interventionnelle, Hôpital de la Timone（艾克斯-马赛大学，APHM，诊断与介入神经放射科，泰米翁医院）；Aix-Marseille Univ, APHM, service de neurologie pédiatrique, Hôpital de la Timone（艾克斯-马赛大学，APHM，儿童神经科，泰米翁医院）
Robot-GST: geometry-aware spatial-temporal robot policy representation and evaluation
KTH（瑞典皇家理工学院）；EPFL（洛桑联邦理工学院）；Idiap Research Institute（Idiap 研究所）；Beihang University（北京航空航天大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Great Bay University（大湾区大学）；The Hong Kong Polytechnic University（香港理工大学）
