Retrieving Time-Series Differences Using Natural Language Queries
This addresses the challenge for system analysts who need to search time-series data without domain expertise, though it is incremental as it builds on existing natural language search methods.
The paper tackles the problem of retrieving pairs of time-series data based on differences specified in natural language queries, achieving an overall mAP score of 0.994.
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.