Abstract
Multivariate time series data, collected across various fields such as manufacturing and wearable technology, exhibit states at multiple levels of granularity, from coarse-grained system behaviors to fine-grained, detailed events. Effectively segmenting and integrating states across these different granularities is crucial for tasks like predictive maintenance and performance optimization. However, existing time series segmentation methods face two key challenges: (1) the inability to handle multiple levels of granularity within a unified model, and (2) limited adaptability to new, evolving patterns in dynamic environments. To address these challenges, we propose PromptTSS, a novel framework for time series segmentation with multi-granularity states. PromptTSS uses a unified model with a prompting mechanism that leverages label and boundary information to guide segmentation, capturing both coarse- and fine-grained patterns while adapting dynamically to unseen patterns. Experiments show PromptTSS improves accuracy by 24.49% in multi-granularity segmentation, 17.88% in single-granularity segmentation, and up to 599.24% in transfer learning, demonstrating its adaptability to hierarchical states and evolving time series dynamics.
Abstract (translated)
多变量时间序列数据在制造和可穿戴技术等各个领域收集，这些数据展示了从粗粒度的系统行为到细粒度的详细事件等多种级别的状态。有效地对不同层次精细程度的状态进行分割和整合对于任务如预测性维护和性能优化至关重要。然而，现有的时间序列分割方法面临两个关键挑战：（1）无法在统一模型中处理多个级别精细度的问题；（2）适应动态环境中新出现、不断变化的模式的能力有限。 为了解决这些挑战，我们提出了PromptTSS框架，这是一种用于具有多级状态的时间序列分割的新颖方法。PromptTSS使用了一种统一模型，并结合了提示机制来利用标签和边界信息引导时间序列的分割过程。这样不仅可以捕捉到粗粒度和细粒度的所有模式，而且还可以动态适应未见过的数据模式。 实验结果显示，与现有技术相比，PromptTSS在多级分割中提高了24.49%的准确性，在单级分割中提高了17.88%，并且在迁移学习中的表现最高提升了599.24%。这些结果表明该框架能够适应分层状态和不断变化的时间序列动态，并具有强大的可扩展性。
URL
https://arxiv.org/abs/2506.11170
