标题： 使用稀疏自编码器学习事件时间序列的表示用于异常检测、相似性搜索和无监督分类 显示英文标题

标题： Learning Representations of Event Time Series with Sparse Autoencoders for Anomaly Detection, Similarity Search, and Unsupervised Classification

摘要： 事件时间序列是发生在不规则时间间隔的离散事件序列，每个事件都与特定领域的观测模态相关联。它们常见于高能天体物理学、计算社会科学、网络安全、金融、医疗保健、神经科学和地震学等领域。其无结构和不规则的结构对使用传统技术提取有意义的模式和识别显著现象构成了重大挑战。我们提出了用于事件时间序列的新颖二维和三维张量表示，并结合了学习物理上有意义潜在表示的稀疏自编码器。这些嵌入支持多种下游任务，包括异常检测、基于相似性的检索、语义聚类和无监督分类。我们在一个来自X射线天文学的真实数据集上展示了我们的方法，表明这些表示能够成功捕捉时间和光谱特征，并分离各种类型的X射线瞬变。我们的框架为分析跨科学和工业领域的复杂、不规则事件时间序列提供了一个灵活、可扩展和可泛化的解决方案。 显示英文摘要

摘要： Event time series are sequences of discrete events occurring at irregular time intervals, each associated with a domain-specific observational modality. They are common in domains such as high-energy astrophysics, computational social science, cybersecurity, finance, healthcare, neuroscience, and seismology. Their unstructured and irregular structure poses significant challenges for extracting meaningful patterns and identifying salient phenomena using conventional techniques. We propose novel two- and three-dimensional tensor representations for event time series, coupled with sparse autoencoders that learn physically meaningful latent representations. These embeddings support a variety of downstream tasks, including anomaly detection, similarity-based retrieval, semantic clustering, and unsupervised classification. We demonstrate our approach on a real-world dataset from X-ray astronomy, showing that these representations successfully capture temporal and spectral signatures and isolate diverse classes of X-ray transients. Our framework offers a flexible, scalable, and generalizable solution for analyzing complex, irregular event time series across scientific and industrial domains.