AI 中文总结
Rondo提出无监督方法，通过构建可重用单元词汇表来发现连续时间流中的重复层次结构，在有限数据和持续流场景下优于现有基线。
AI 中文摘要
许多现实世界的时间序列数据在多个尺度上表现出结构特性，从短小的重复单元到由这些单元组成的复杂序列。无监督地发现这些组件和结构，有助于设计智能系统来解读时间数据，从而减少所需的高成本人工标注量。现有的建模方法通常忽视了许多时间序列固有的层次结构，将不同时间尺度上的重复模式视为独立结构。此外，大多数方法假设可以访问完整的数据序列，并将发现过程视为静态过程，限制了其随着新观测数据到达而演进的能力。我们引入了Rondo，一种用于在连续时间流中建模重复层次结构的无监督方法。通过显式构建可重用单元及其重复组合的词汇表，Rondo捕获了复杂时间模式之间共享的结构，同时随着流的演进不断细化和扩展其发现。在跨越不同领域和数据模态的时间序列上的评估表明，Rondo优于现有的无监督重复发现基线，在数据有限和持续流设置中优势尤为明显。这些能力为重复模式发现、可扩展行为理解以及在长无标注时间流上运行的适应性智能系统提供了更坚实的基础。
英文摘要
Many real-world time series data exhibit structural properties at multiple scales, from short, recurring units to complex sequences composed of these units. Unsupervised discovery of both these components and structure enables the design of intelligent systems that help interpret temporal data, thereby limiting the amount of costly human annotations required. Existing modeling approaches typically overlook the hierarchical structure inherent to many time series, treating recurring patterns at different temporal scales as independent structures. Moreover, most assume access to the complete data sequence and treat discovery as a static process, limiting their ability to evolve as new observations arrive. We introduce Rondo, an unsupervised approach for modeling recurring hierarchical structure in continuous temporal streams. By explicitly constructing vocabularies of reusable units and their recurring compositions, Rondo captures structure shared across complex temporal patterns while refining and expanding its discoveries as the stream evolves. Evaluations on temporal sequences spanning diverse domains and data modalities show that Rondo outperforms existing unsupervised recurrence-discovery baselines, with particularly pronounced advantages in limited-data and continual-stream settings. These capabilities provide a stronger foundation for recurring-pattern discovery, scalable behavior understanding, and adaptive intelligent systems operating on long, unlabeled temporal streams.
