5 papers · 1 filter
LaSTR: Language-Driven Time-Series Segment Retrieval
Kota Dohi, Harsh Purohit, Tomoya Nishida +6
Effectively searching time-series data is essential for system analysis, but existing methods often require expert-designed similarity criteria or rely on global, series-level desc…
DiffNator: Generating Structured Explanations of Time-Series Differences
Kota Dohi, Tomoya Nishida, Harsh Purohit +2
In many IoT applications, the central interest lies not in individual sensor signals but in their differences, yet interpreting such differences requires expert knowledge. We propo…
CLaSP: Learning Concepts for Time-Series Signals from Natural Language Supervision
Aoi Ito, Kota Dohi, Yohei Kawaguchi
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 s…
Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data
Kota Dohi, Aoi Ito, Harsh Purohit +3
Due to scarcity of time-series data annotated with descriptive texts, training a model to generate descriptive texts for time-series data is challenging. In this study, we propose…
Retrieving Time-Series Differences Using Natural Language Queries
Kota Dohi, Tomoya Nishida, Harsh Purohit +2
Effectively searching time-series data is essential for system analysis; however, traditional methods often require domain expertise to define search criteria. Recent advancements…