2024/11/13 by Akihiko Ito, Ito, Aoi, Kota Dohi +3 · 2 citations
Computer Science · #Advanced Computational Techniques and Applications #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Seismology and Earthquake Studies #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2411.08397
openalex publication_date 2024/11/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.