T-Rep: Representation Learning for Time Series using Time-Embeddings
Let it Care；PariSanté Campus（巴黎健康校园）；Université Paris Cité（巴黎西岱大学）；INRIA（法国国家信息与自动化研究所）；Inserm（法国国家健康与医学研究院）；Centre de Recherche des Cordeliers（科德利埃研究所）
Multivariate time series present challenges to standard machine learning techniques, as they are often unlabeled, high dimensional, noisy, and contain missing data. To address this, we propose T-Rep, a self-supervised method to learn time series representations at a timestep granularity. T-Rep learns vector embeddings of time alongside its feature extractor, to extract temporal features such as trend, periodicity, or distribution shifts from the signal. These time-embeddings are leveraged in pretext tasks, to incorporate smooth and fine-grained temporal dependencies in the representations, as well as reinforce robustness to missing data. We evaluate T-Rep on downstream classification, forecasting, and anomaly detection tasks. It is compared to existing self-supervised algorithms for time series, which it outperforms in all three tasks. We test T-Rep in missing data regimes, where it proves more resilient than its counterparts. Finally, we provide latent space visualisation experiments, highlighting the interpretability of the learned representations.
Frequency-Masked Embedding Inference: A Non-Contrastive Approach for Time Series Representation Learning
School of Intelligence Science and Technology, University of Science and Technology Beijing（北京科技大学智能科学与技术学院）；Institute of Artificial Intelligence, University of Science and Technology Beijing（北京科技大学人工智能研究院）
Higher-order Cross-structural Embedding Model for Time Series Analysis
School of Mathematics and Statistics, Beijing Jiaotong University（北京交通大学数学与统计学院）；School of Physical and Mathematical Sciences, Nanyang Technological University（南洋理工大学物理与数学科学学院）；Department of Mathematics, National University of Singapore（新加坡国立大学数学系）
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting
University of Science and Technology of China（中国科学技术大学）；Institute of Advanced Technology, University of Science and Technology of China（中国科学技术大学先进技术研究院）；Jiaxing University（嘉兴学院）
Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask
Motherbrain, EQT Group（Motherbrain、EQT集团）；KTH Royal Institute of Technology（皇家理工学院）；Mitsubishi UFJ Trust Investment Technology Institute（三菱UFJ信托投资技术研究所）；Technical University of Denmark（丹麦技术大学）
QKCV Attention: Enhancing Time Series Forecasting with Static Categorical Embeddings for Both Lightweight and Pre-trained Foundation Models
Independent Researcher（独立研究者）；Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai)（信息行为脑机智能关键实验室）；School of Business and Management（商学院）
Combining Embeddings and Fuzzy Time Series for High-Dimensional Time Series Forecasting in Internet of Energy Applications
Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）；Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；Federal Institute of Education Science and Technology of Northern Minas Gerais（北米纳斯吉拉斯联邦教育科学技术学院）
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）
A Time-Series Foundation Model by Universal Delay Embedding
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences（中国科学院大学杭州高等研究院）；Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；Ocean University of China（中国海洋大学）；South China University of Technology（华南理工大学）
Temporal Fusion Nexus: A task-agnostic multi-modal embedding model for clinical narratives and irregular time series in post-kidney transplant care
Hahn-Schickard（哈恩-施克特德研究所）；University of Freiburg（弗赖堡大学）；Charité University Medical Center（查理医院大学医学中心）；Hasso Plattner Institute for Digital Engineering, University of Potsdam（哈索·platner数字工程研究所，波茨坦大学）；DFKI（德意志联邦防务研究院）；University Hospital Essen（埃森大学医院）；University Hospital Erlangen（埃尔兰根大学医院）
Integrating Time Series into LLMs via Multi-layer Steerable Embedding Fusion for Enhanced Forecasting
School of Software Engineering Sun Yat-Sen University（软件工程学院中山大学）；Institute of Data Science（数据科学研究院）；School of Computing（计算机学院）；National University of Singapore（新加坡国立大学）
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）
