This paper considers the problem of learning, from samples, the dependency structure of a system of linear stochastic differential equations, when some of the variables are latent. In particular, we observe the time evolution of some variables, and never observe other variables; from this, we would like to find the dependency structure between the observed variables - separating out the spurious interactions caused by the (marginalizing out of the) latent variables' time series. We develop a new method, based on convex optimization, to do so in the case when the number of latent variables is smaller than the number of observed ones. For the case when the dependency structure between the observed variables is sparse, we theoretically establish a high-dimensional scaling result for structure recovery. We verify our theoretical result with both synthetic and real data (from the stock market).
The Trade-off Between Covariate Dependence and Latent Structure in Representation Learning
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw（华沙大学数学、信息学与力学学院）；Institute of AI for Health, Helmholtz Munich（亥姆霍兹慕尼黑健康人工智能研究所）
Anomaly Detection for Sparse and Irregular Multivariate Time Series with Latent SDEs
Josef Ressel Centre for Intelligent and Secure Industrial Automation, University of Applied Sciences, Salzburg, Austria（约瑟夫·雷斯尔智能与安全工业自动化中心，应用科学大学，萨尔茨堡，奥地利）；University of Salzburg, Austria（萨尔茨堡大学，奥地利）
Latent Processes Identification From Multi-View Time Series
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；State Key Lab of Brain-Machine Intelligence, Zhejiang University（浙江大学脑机智能全国重点实验室）；CCAI by MOE and Zhejiang Provincial Government (ZJU)（教育部与浙江省共建中国人工智能研究院（浙江大学））
Online Time Series Forecasting with Theoretical Guarantees
Carnegie Mellon University（卡内基梅隆大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University of Technology（广东技术大学）；Johns Hopkins University（约翰霍普金斯大学）；University of California San Diego（加州大学圣地亚哥分校）
Hierarchical Stochastic Differential Equation Models for Latent Manifold Learning in Neural Time Series
Department of Biomedical Engineering（生物医学工程系）；University of Houston（休斯顿大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Isfahan University of Technology（伊斯法罕技术大学）
Nonstationary Time Series Forecasting via Unknown Distribution Adaptation
Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Guangdong University（广东大学）；Carnegie Mellon University（卡内基梅隆大学）；Shantou University（汕头大学）
From Orthogonality to Dependency: Learning Disentangled Representation for Multi-Modal Time-Series Sensing Signals
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Shantou University（汕头大学）；Carnegie Mellon University（卡内基梅隆大学）
EIDOS: Latent-Space Predictive Learning for Time Series Foundation Models
Nankai University（南开大学）；Eindhoven University of Technology（埃因霍温理工大学）；Yunnan University（云南大学）；University of New South Wales（新南威尔士大学）；Griffith University（格里菲斯大学）
Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting
Department of Computer Science, Aalborg University（奥尔堡大学计算机科学系）；Department of Computer Science, East China Normal University（华东师范大学计算机科学系）；Huawei Cloud Database Innovation Lab（华为云数据库创新实验室）；University of Electronic Science and Technology of China（电子科技大学）
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
Department of Computer Science Norwegian University of Science and Technology（挪威科学技术大学计算机科学系）；BCAM – Basque Center for Applied Mathematics University of Coimbra, CISUC/LASI, DEI（巴斯克应用数学中心，科英布拉大学，CISUC/LASI，DEI）；IKERBASQUE — Basque Foundation for Science（巴斯克科学基金会）
ALER-TI: Aligned Latent Embedding Retrieval for Time Series Imputation
School of Computer Science, Hanoi University of Science and Technology（河内科学技术大学计算机科学学院）；Department of Computer Science, Aalborg University（奥尔堡大学计算机科学系）
StoxLSTM: A Stochastic Extended Long Short-Term Memory Network for Time Series Forecasting
School of Information and Electronics（信息与电子学院）；Beijing Institute of Technology（北京理工大学）；Laboratory of Electromagnetic Space Cognition and Intelligent Control（电磁空间认知与智能控制实验室）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）
Time Series Domain Adaptation via Latent Invariant Causal Mechanism
School of Computer Science, Guangdong University of Technology（广东工业大学计算机学院）；Peng Cheng Laboratory（鹏城实验室）；School of Computing, Guangdong University of Technology（广东工业大学计算机学院）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Institute for Infocomm Research, A*STAR（新加坡科技研究局信息通信研究所）；Centre for Frontier AI Research, A*STAR（新加坡科技研究局前沿人工智能研究中心）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）
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）
L-GTA: Latent Generative Modeling for Time Series Augmentation
Faculdade de Engenharia da Universidade do Porto（葡萄牙波尔图大学工程学院）；Fraunhofer Portugal AICOS（德国弗劳恩霍夫葡萄牙AICOS研究所）；University of Coimbra（科英布拉大学）；Dalhousie University（达尔豪斯大学）
