用自然语言描述检索时间序列信号，无需预设词典。
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
- 通过对比学习将时序信号映射到自然语言描述。
- 在TRUCE和SUSHI数据集上实现高精度查询检索。
- 适合作为工业诊断中快速定位特征信号的工具。
本文提出CLaSP，一种基于自然语言描述检索时间序列信号的新模型。在工业诊断等领域，数据科学家常需查找具有特定特征的信号，但现有方法依赖草图输入、预定义同义词词典或领域定制设计，难以扩展和适应。CLaSP采用对比学习，将时序信号与自然语言描述对齐，无需预设词典，充分借助大语言模型的上下文知识。在包含时序信号与自然语言描述配对的TRUCE和SUSHI数据集上，CLaSP实现了对多种时序模式的高精度检索，验证了其有效性与通用性。
原文摘要 · Abstract (English)
This paper presents CLaSP, a novel model for retrieving time-series signals using natural language queries that describe signal characteristics. The ability to search time-series signals based on descriptive queries is essential in domains such as industrial diagnostics, where data scientists often need to find signals with specific characteristics. However, existing methods rely on sketch-based inputs, predefined synonym dictionaries, or domain-specific manual designs, limiting their scalability and adaptability. CLaSP addresses these challenges by employing contrastive learning to map time-series signals to natural language descriptions. Unlike prior approaches, it eliminates the need for predefined synonym dictionaries and leverages the rich contextual knowledge of large language models (LLMs). Using the TRUCE and SUSHI datasets, which pair time-series signals with natural language descriptions, we demonstrate that CLaSP achieves high accuracy in retrieving a variety of time series patterns based on natural language queries.
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。