Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
#Sonar-TS#NLQ4TSDB#Time‑Series Database#Natural Language Query#Neuro‑symbolic Framework#Search‑Then‑Verify#Active Sonar#SQL#Python#NLQTSBench#Large‑Scale Benchmark
📌 Key Takeaways
The study identifies key limitations of current Text‑to‑SQL and time‑series models for handling continuous morphological intents and ultra‑long histories.
Sonar‑TS, a neuro‑symbolic framework, restructures the problem into a Search‑Then‑Verify pipeline based on an active‑sonar analogy.
A feature‑index is used to ping candidate windows via SQL, after which generated Python programs lock onto and verify these windows against raw signal data.
The authors introduce NLQTSBench, a large‑scale benchmark specifically designed for natural‑language querying over time‑series databases at scale.
Experimental results highlight the unique challenges of the NLQ4TSDB domain and demonstrate that Sonar‑TS succeeds where conventional approaches falter.
The paper presents the first systematic study of NLQ4TSDB, providing both a general framework and an evaluation standard.
It opens pathways for future research in integrating symbolic SQL reasoning with neural signal processing for complex temporal queries.
📖 Full Retelling
The paper *Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases* was authored by Zhao Tan, Yiji Zhao, Shiyu Wang, Chang Xu, Yuxuan Liang, Xiping Liu, Shirui Pan, and Ming Jin, and posted on arXiv on 19 February 2026. It introduces a neuro‑symbolic framework that tackles the challenge of natural‑language querying over large‑scale time‑series databases (NLQ4TSDB). Existing Text‑to‑SQL methods struggle with continuous morphological intents such as shapes or anomalies, and typical time‑series models cannot cope with ultra‑long histories; Sonar‑TS addresses these gaps by emulating active sonar – first pinging candidate windows with a feature index via SQL and then locking in with generated Python programs that verify candidates against raw signals. The authors also present NLQTSBench, the first large‑scale benchmark for NLQ over TSDB‑scale histories, and show through experiments that Sonar‑TS can navigate complex temporal queries where traditional methods fail.
In sum, the paper provides the first systematic study of natural‑language querying for time‑series databases, offers a general framework, and establishes a new evaluation standard for future research.
🏷️ Themes
Time Series Databases, Natural Language Querying, Neuro‑Symbolic AI, Search‑Then‑Verify Pipeline, Active‑Sonar Analogy, Benchmarking, Handling Ultra‑Long Histories
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Deep Analysis
Why It Matters
Sonar-TS introduces a novel search-then-verify pipeline that enables natural language querying of time-series databases, a capability that was previously limited by traditional SQL and time-series models. By combining a feature index with generated Python programs, it allows non-experts to retrieve complex temporal events, intervals, and summaries from massive datasets. This advances data accessibility and supports decision-making in fields such as finance, IoT, and healthcare.
Context & Background
Time-series data is growing rapidly in volume and complexity
Existing text-to-SQL methods struggle with continuous morphological intents like shapes and anomalies
There is a lack of standardized benchmarks for natural language querying over time-series databases
What Happens Next
The introduction of NLQTSBench provides a community-ready benchmark that will spur further research and comparison of NLQ4TSDB methods. Researchers are expected to build on Sonar-TS’s framework to develop more efficient indexing and verification techniques, potentially integrating deep learning models for richer semantic understanding.
Frequently Asked Questions
What problem does Sonar-TS solve?
It enables natural language queries to retrieve meaningful events, intervals, and summaries from large time-series databases, overcoming limitations of existing text-to-SQL and time-series models.
How does the Search-Then-Verify pipeline work?
First, a feature index pings candidate windows via SQL; then, generated Python programs lock on and verify candidates against raw signals, ensuring accurate retrieval.
Original Source
--> Computer Science > Artificial Intelligence arXiv:2602.17001 [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 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 of NLQ4TSDB, offering a general framework and evaluation standard to facilitate future research. Subjects: Artificial Intelligence (cs.AI) ; Computation and Language (cs.CL cs.DB) Cite as: arXiv:2602.17001 [cs.AI] (or arXiv:2602.17001v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2602.17001 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Zhao Tan [ view email ] [v1] Thu, 19 Feb 2026 01:51:52 UTC (1,874 KB) Full-text links: Access Paper: View ...
