用稀疏自编码器学习事件时间序列的高维表示，提升异常检测与聚类效果
Learning Representations of Event Time Series with Sparse Autoencoders for Anomaly Detection, Similarity Search, and Unsupervised Classification
- 构建二维/三维张量表征事件时间序列，捕捉时间与频谱特征
- 在天文数据上实现多种瞬态源分类，准确识别异常事件
- 适用于跨领域不规则事件分析，尤其适合无监督场景
事件时间序列是由不规则时间间隔发生的离散事件构成，具有领域特定的观测模态，广泛存在于高能天体物理、计算社会科学、网络安全、金融、医疗、神经科学和地震学等领域。其非结构化和不规则性给传统方法提取有意义模式带来挑战。本文提出新型二维与三维张量表示方法，结合稀疏自编码器学习具有物理意义的潜在表征。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。我们在一个真实的X射线天文学数据集上验证了该方法，结果表明其成功捕获了时间和光谱特征，并有效分离出多种类型的X射线暂现源。本框架为跨科学与工业领域的复杂不规则事件时间序列分析提供了一种灵活、可扩展且通用的解决方案。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。