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[2602.17001] Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
Summary
Sonar-TS introduces a neuro-symbolic framework for natural language querying of time series databases, addressing limitations of existing methods in handling complex temporal queries.
Why It Matters
This research is significant as it proposes a novel approach to querying time series data, which is increasingly important in various fields such as finance, healthcare, and IoT. By tackling the challenges of continuous morphological intents and ultra-long histories, Sonar-TS enhances accessibility for non-expert users and sets a new benchmark for future research in this domain.
Key Takeaways
- Sonar-TS utilizes a Search-Then-Verify pipeline to improve natural language querying for time series databases.
- The framework addresses challenges that traditional Text-to-SQL methods face with complex temporal queries.
- NLQTSBench is introduced as the first large-scale benchmark for evaluating natural language querying over time series databases.
- The study highlights the unique difficulties in querying time series data, paving the way for future advancements.
- Sonar-TS represents a systematic approach to natural language querying, enhancing usability for non-expert users.
Computer Science > Artificial Intelligence arXiv:2602.17001 (cs) [Submitted on 19 Feb 2026] Title:Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases Authors:Zhao Tan, Yiji Zhao, Shiyu Wang, Chang Xu, Yuxuan Liang, Xiping Liu, Shirui Pan, Ming Jin View a PDF of the paper titled Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases, by Zhao Tan and 7 other authors View PDF HTML (experimental) Abstract:Natural Language Querying for Time Series Databases (NLQ4TSDB) aims to assist non-expert users retrieve meaningful events, intervals, and summaries from massive temporal records. However, existing Text-to-SQL methods are not designed for continuous morphological intents such as shapes or anomalies, while time series models struggle to handle ultra-long histories. To address these challenges, we propose Sonar-TS, a neuro-symbolic framework that tackles NLQ4TSDB via a Search-Then-Verify pipeline. Analogous to active sonar, it utilizes a feature index to ping candidate windows via SQL, followed by generated Python programs to lock on and verify candidates against raw signals. To enable effective evaluation, we introduce NLQTSBench, the first large-scale benchmark designed for NLQ over TSDB-scale histories. Our experiments highlight the unique challenges within this domain and demonstrate that Sonar-TS effectively navigates complex temporal queries where traditional methods fail. This work presents the first systematic study ...
