xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links
Institute of Informatics, LMU Munich（慕尼黑大学信息学研究所）；Corporate Technology, Siemens AG（西门子股份公司中央研究院）；Department of Informatics, Technical University of Munich（慕尼黑工业大学信息学系）
Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on temporal KGs. However, the embedding-based approaches largely operate in a black-box fashion, lacking the ability to interpret their predictions. This paper provides a link forecasting framework that reasons over query-relevant subgraphs of temporal KGs and jointly models the structural dependencies and the temporal dynamics. Especially, we propose a temporal relational attention mechanism and a novel reverse representation update scheme to guide the extraction of an enclosing subgraph around the query. The subgraph is expanded by an iterative sampling of temporal neighbors and by attention propagation. Our approach provides human-understandable evidence explaining the forecast. We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task. While being more explainable, our model obtains a relative improvement of up to 20% on Hits@1 compared to the previous best KG forecasting method. We also conduct a survey with 53 respondents, and the results show that the evidence extracted by the model for link forecasting is aligned with human understanding.
Siemens AG（西门子股份公司）；Ludwig Maximilian University of Munich（慕尼黑大学）
Conventional static knowledge graphs model entities in relational data as nodes, connected by edges of specific relation types. However, information and knowledge evolve continuously, and temporal dynamics emerge, which are expected to influence future situations. In temporal knowledge graphs, time information is integrated into the graph by equipping each edge with a timestamp or a time range. Embedding-based methods have been introduced for link prediction on temporal knowledge graphs, but they mostly lack explainability and comprehensible reasoning chains. Particularly, they are usually not…
University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Argonne National Laboratory（阿贡国家实验室）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans multiple hours remains a major challenge. Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability. We develop Graph Pyramid Autoformer (X-GPA), an explainable attention-based spatial-temporal graph neural network that uses a novel pyramid auto…
Jožef Stefan Institute（约瑟夫·斯特凡研究所）；Qlector d.o.o.（Qlector 有限公司）；Jožef Stefan International Postgraduate School（约瑟夫·斯特凡国际研究生院）
The paper proposes a novel architecture for explainable AI based on semantic technologies and AI. We tailor the architecture for the domain of demand forecasting and validate it on a real-world case study. The provided explanations combine concepts describing features relevant to a particular forecast, related media events, and metadata regarding external datasets of interest. The knowledge graph provides concepts that convey feature information at a higher abstraction level. By using them, explanations do not expose sensitive details regarding the demand forecasting models. The explanations a…
MCDFN: Supply Chain Demand Forecasting via an Explainable Multi-Channel Data Fusion Network Model
Khulna University of Engineering and Technology (KUET)（库尔纳工程技术大学）；Okinawa Institute of Science and Technology Graduate University (OIST)（冲绳科学技术大学院大学）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
An Explainable, Attention-Enhanced, Bidirectional Long Short-Term Memory Neural Network for Joint 48-Hour Forecasting of Temperature, Irradiance, and Relative Humidity
Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years. We propose the Deep Attention Unistream Multistream (DAUM) networks that investigate different types of input representations (i.e. tensorial unistream vs. multistream ) as well as the incorporation of the attention mechanism. In particular, we show that adding a self-attention…
We introduce NeuralProphet, a successor to Facebook Prophet, which set an industry standard for explainable, scalable, and user-friendly forecasting frameworks. With the proliferation of time series data, explainable forecasting remains a challenging task for business and operational decision making. Hybrid solutions are needed to bridge the gap between interpretable classical methods and scalable deep learning models. We view Prophet as a precursor to such a solution. However, Prophet lacks local context, which is essential for forecasting the near-term future and is challenging to extend due…
Ryerson University（瑞尔森大学）；University of Toronto（多伦多大学）；Bogazici University（博阿齐奇大学）
Time series forecasting involves collecting and analyzing past observations to develop a model to extrapolate such observations into the future. Forecasting of future events is important in many fields to support decision making as it contributes to reducing the future uncertainty. We propose explainable boosted linear regression (EBLR) algorithm for time series forecasting, which is an iterative method that starts with a base model, and explains the model's errors through regression trees. At each iteration, the path leading to highest error is added as a new variable to the base model. In th…
Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction
University of Science and Technology of Oran-Mohamed Boudiaf (USTO-MB)（奥兰科技大学-穆罕默德·布迪亚夫）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Jožef Stefan International Postgraduate School（约热夫·斯特凡国际研究生院）；Jožef Stefan Institute（约热夫·斯特凡研究所）；Qlector d.o.o.（Qlector 有限责任公司）
The increasing adoption of artificial intelligence requires accurate forecasts and means to understand the reasoning of artificial intelligence models behind such a forecast. Explainable Artificial Intelligence (XAI) aims to provide cues for why a model issued a certain prediction. Such cues are of utmost importance to decision-making since they provide insights on the features that influenced most certain forecasts and let the user decide if the forecast can be trusted. Though many techniques were developed to explain black-box models, little research was done on assessing the quality of thos…
Accurately forecasting patient arrivals at Urgent Care Clinics (UCCs) and Emergency Departments (EDs) is important for effective resourcing and patient care. However, correctly estimating patient flows is not straightforward since it depends on many drivers. The predictability of patient arrivals has recently been further complicated by the COVID-19 pandemic conditions and the resulting lockdowns. This study investigates how a suite of novel quasi-real-time variables like Google search terms, pedestrian traffic, the prevailing incidence levels of influenza, as well as the COVID-19 Alert Level …
SPINEX-TimeSeries: Similarity-based Predictions with Explainable Neighbors Exploration for Time Series and Forecasting Problems
University of Manitoba（曼尼托巴大学）；Clemson University（克莱姆森大学）；Artificial Intelligence Research Institute for Science and Engineering (AIRISE), Clemson University（克莱姆森大学科学与工程人工智能研究院（AIRISE））
Causal Interventional Prediction System for Robust and Explainable Effect Forecasting
Zhejiang University（浙江大学）；Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security（杭州高新区（滨江）区块链与数据安全研究院）；Ant Group（蚂蚁集团）；Nanyang Technological University（南洋理工大学）
Exploring Machine Learning, Deep Learning, and Explainable AI Methods for Seasonal Precipitation Prediction in South America
Laboratório de Inteligência ARtificial para Aplicações AeroEspaciais e Ambientais (LIAREA)（人工智能应用航空与环境实验室）；Coordenação de Pesquisa Aplicada e Desenvolvimento Tecnológico (COPDT)（应用研究与技术开发协调处）；Programa de Pós-Graduação em Computação Aplicada (PGCAP)（应用计算硕士项目）；Instituto Nacional de Pesquisas Espaciais (INPE)（国家空间科学研究院）；Instituto de Estudos Avançados (IEAv)（高级研究学院）
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
University of Cambridge（剑桥大学）；Mila - Quebec AI Institute（魁北克人工智能研究院）；McGill University（麦吉尔大学）；University of Oxford（牛津大学）；National University of Singapore（新加坡国立大学）；University of Stuttgart（斯图加特大学）；Amazon（亚马逊公司）；AITHYRA
A Transformer variant for multi-step forecasting of water level and hydrometeorological sensitivity analysis based on explainable artificial intelligence technology
School of Internet, Anhui University（安徽大学互联网学院）；National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University（安徽大学农业生态大数据分析与应用国家工程研究中心）；Forschungszentrum Jülich GmbH, Institute of Energy and Climate Research (IEK)（于利希研究中心能源与气候研究所）；Hefei Institutes of Physical Science, Chinese Academy of Sciences（中国科学院合肥物质科学研究院）
Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models
Central University of Finance and Economics（中央财经大学）；New York University（纽约大学）；Harvard University（哈佛大学）；Columbia University（哥伦比亚大学）；University of Michigan（密歇根大学）
Explainable Earth Surface Forecasting under Extreme Events
Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University（莱比锡大学地球系统研究遥感中心）；Institute for Earth System Science and Remote Sensing, Leipzig University（莱比锡大学地球系统科学与遥感研究所）；Image and Signal Processing Group, Leipzig University（莱比锡大学图像与信号处理组）；Helmholtz Centre for Environmental Research – UFZ（亥姆霍兹环境研究中心）
Interpretable A-posteriori Error Indication for Graph Neural Network Surrogate Models
Transportation and Power Systems Division, Argonne National Laboratory（阿贡国家实验室交通与动力系统分部）；College of Information Sciences and Technology, Pennsylvania State University（宾夕法尼亚州立大学信息科学与技术学院）；Mathematics and Computer Science Division, Argonne National Laboratory（阿贡国家实验室数学与计算机科学分部）
KASPER: Kolmogorov Arnold Networks for Stock Prediction and Explainable Regimes
Vishwakarma Government Engineering College（维斯瓦卡罗马工程学院）；QuantumAI Lab, Fractal Analytics（量子AI实验室，Fractal Analytics）；eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD)（eBRAIN实验室，工程系，纽约大学阿布扎比分校（NYUAD））；Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute（量子与拓扑系统中心（CQTS），NYUAD研究机构）
Explainable Forecasting of Scientific Breakthroughs from Concept Network Dynamics
Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland（日内瓦经济管理学院，日内瓦大学，瑞士日内瓦）；Faculty of Medicine, University of Geneva, Geneva, Switzerland（日内瓦大学医学学院，瑞士日内瓦）；TU Eindhoven, The Netherlands（埃因霍温理工大学，荷兰）；Open Quantum Institute, CERN, Geneva, Switzerland（开放量子研究所，欧洲核子研究中心，瑞士日内瓦）；armasuisse Science + Technology, Switzerland（armasuisse 科学与技术，瑞士）
Forecasting the future development in quality and value of professional football players
1 Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands；\ 2 Data Science \& Computer Vision, SciSports, 3703 HX Zeist, The Netherlands
Knowledge-Guided Machine Learning: Illustrating the use of Explainable Boosting Machines to Identify Overshooting Tops in Satellite Imagery
The Courant Institute of Mathematical Sciences, New York University（Courant数学科学研究所，纽约大学）；Department of Statistics, Colorado State University（统计学系，科罗拉多州立大学）；Cooperative Institute for Research in the Atmosphere, Colorado State University（大气研究合作研究所，科罗拉多州立大学）；Department of Electrical and Computer Engineering, Colorado State University（电气与计算机工程系，科罗拉多州立大学）；Department of Mathematics, Colorado State University（数学系，科罗拉多州立大学）；School of Earth and Environmental Sciences, Seoul National University（地球与环境科学学院，首尔国立大学）
