用自然语言查时间序列差异，精准定位数据变化点
Retrieving Time-Series Differences Using Natural Language Queries
- 通过对比学习对齐查询语句与时间序列差异特征
- 在六类差异特征上实现0.994的mAP检索性能
- 适合系统分析、运维监控等需快速发现异常的场景
有效搜索时间序列数据对系统分析至关重要；然而传统方法通常需要领域知识来定义搜索条件。近年来，基于自然语言的搜索已取得进展，但难以处理时间序列之间的差异。为解决这一问题，我们提出一种基于自然语言查询的时间序列对检索方法，通过指定查询中的差异内容进行检索。具体而言，我们定义了六种关键差异特征，构建了对应数据集，并设计了一种基于对比学习的模型，将时间序列间的差异与查询文本对齐。实验结果表明，该模型在检索时间序列对时取得了0.994的总体mAP得分。
原文摘要 · Abstract (English)
Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements have enabled natural language-based search, but these methods struggle to handle differences between time-series data. To address this limitation, we propose a natural language query-based approach for retrieving pairs of time-series data based on differences specified in the query. Specifically, we define six key characteristics of differences, construct a corresponding dataset, and develop a contrastive learning-based model to align differences between time-series data with query texts. Experimental results demonstrate that our model achieves an overall mAP score of 0.994 in retrieving time-series pairs.
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