We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner. Motivated by the different working mechanisms within these two research lines, we aim to connect them in such a way as to jointly model time-series similarities and learn the representations. This is a challenging task because it is unclear how we should efficiently leverage similarity information. To tackle the challenge, we propose Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and general framework that models similarity information with graph neural networks (GNNs). Specifically, we formulate TSC as a node classification problem in graphs, where the nodes correspond to time-series, and the links correspond to pair-wise similarities. We further design a graph construction strategy and a batch training algorithm with negative sampling to improve training efficiency. We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping (DTW) as the similarity measure. Extensive experiments on the full UCR datasets and several multivariate datasets demonstrate the effectiveness of incorporating similarity information into deep learning models in both supervised and semi-supervised settings. Our code is available at https://github.com/daochenzha/SimTSC
Dynamic Graph with Similarity-Aware Attention Graph Neural Network for Recommender Systems
Department of Computer Science and Engineering（计算机科学与工程系）；SRM University Andhra Pradesh（安得拉邦SRM大学）；Mangalagiri, Andhra Pradesh 522502, India（安得拉邦马加拉加里522502, 印度）
Advancing multivariate time series similarity assessment: an integrated computational approach
International Centre of Insect Physiology and Ecology (icipe)（国际昆虫生理与生态中心）；Faculty of Science, University of Dschang（大学丁加角学院）；University of KwaZulu-Natal（夸祖鲁-纳塔尔大学）；African Union Inter-African Phytosanitary Council (AU-IAPSC)（非洲联盟国际非洲植物检疫委员会）
The impact of data set similarity and diversity on transfer learning success in time series forecasting
Fraunhofer Institute for Integrated Circuits IIS（弗劳恩霍夫集成电路研究所）；Fraunhofer Center for Applied Research on Supply Chain Services SCS（弗劳恩霍夫供应链服务应用研究中心）；Christian-Albrechts-Universität zu Kiel（基尔基督教阿尔布雷希特大学）；Otto-Friedrich-Universität Bamberg（班贝格奥托-弗里德里希大学）；Vives University of Applied Science（维维斯应用科技大学）；University of Haute-Alsace（上阿尔萨斯大学）；Monash University（莫纳什大学）
Structure-aware Hybrid-order Similarity Learning for Multi-view Unsupervised Feature Selection
Joint Laboratory of Data Science and Business Intelligence, School of Statistics and Data Science, Southwestern University of Finance and Economics（数据科学与商务智能联合实验室，统计与数据科学学院，西南财经大学）；Big Data Laboratory on Financial Security and Behavior, SWUFE (Laboratory of Philosophy and Social Sciences, Ministry of Education)（金融安全与行为大数据实验室，SWUFE（哲学社会科学实验室，教育部））；Department of Electrical Engineering and Computer Science, University of Kansas（电气与计算机工程系，堪萨斯大学）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算与人工智能学院，西南交通大学）
Cluster-Aware Similarity Diffusion for Instance Retrieval
University of Science and Technology of China（中国科学技术大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）
H+: An Efficient Similarity-Aware Aggregation for Byzantine Resilient Federated Learning
Beijing Institute of Technology（北京理工大学）；Southwest University（西南大学）；Sun Yat-Sen University（中山大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））
TagBO: LLM-Driven Task-Aware Graph Bayesian Optimization for Scientific Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Harbin Institute of Technology（哈尔滨工业大学）；Fudan University（复旦大学）；Shenzhen Loop Area Institute（深圳河套学院）；Shanghai Jiao Tong University（上海交通大学）；Central South University（中南大学）；Huazhong Agricultural University（华中农业大学）
交通时间序列预测是具有挑战性的，因为来自不同位置的复杂时空动态时间序列通常具有不同的模式;并且对于相同的时间序列，模式可能随时间而变化，例如，在一天中存在显示出更强的时间相关性的某些时段。尽管最近的预测模型，特别是基于深度学习的模型，显示出有希望的结果，但它们受到时空不可知论的影响。这样的时空不可知模型采用共享参数空间，而不管时间序列位置和时间段，并且它们假设时间模式在位置之间是相似的，并且不会在时间上演变，这可能并不总是成立，从而导致次优结果。在这项工作中，我们提出了一个框架，旨在把时空不可知模型时空感知模型。为此，我们将来自不同位置的时间序列编码为随机变量，从中生成特定于位置和随时间变化的模型参数，以更好地捕捉时空动态。我们展示了如何整合的框架与规范的注意，使时空意识的注意。其次，为了补偿时空感知模型参数生成过程中引入的额外开销，提出了一种新的窗口注意机制，将复杂度从二次降低到线性，使得时空感知注意也具有竞争效率.我们在四个交通时间序列数据集上展示了强有力的经验证据，其中所提出的时空感知注意力在准确性和效率方面优于最先进的方法。这是“Towards Spatio-Temporal Aware Traffic Time Series Forecasting”的扩展版本，将出现在ICDE 2022 [1]中，包括其他实验结果。
Structural-Aware Sentence Similarity with Recursive Optimal Transport
Research Institute of Information Technology（信息技术研究所）；Beijing National Research Center for Information Science and Technology (BNRist)（北京信息科学与技术国家研究中心）；Tsinghua University（清华大学）
Time Series Similarity Score Functions to Monitor and Interact with the Training and Denoising Process of a Time Series Diffusion Model applied to a Human Activity Recognition Dataset based on IMUs
AI for Sensor Data Analytics Research Group, Ulm University of Applied Sciences（人工智能传感器数据解析研究组，乌尔姆应用科学大学）
Exact Indexing of Time Series under Dynamic Time Warping
College of Equipment Management and UAV Engineering, Air Force Engineering University（空军工程大学装备管理与无人机工程学院）；Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University（西北工业大学光学影像分析与学习中心（OPTIMAL））
