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Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases

arXiv 2602.17001 ↗First posted 19 Feb 2026cs.AI cs.CL cs.DBcited by 0

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Abstract (arXiv)

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.

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References · 22

  1. 2023Time-LLM: Time Series Forecasting by Reprogramming Large Language Modelscited by 693
  2. 2023Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLscited by 680
  3. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 472
  4. 2024CHESS: Contextual Harnessing for Efficient SQL Synthesiscited by 167
  5. 2023MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQLcited by 162
  6. 2024CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQLcited by 137
  7. 2024ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoningcited by 97
  8. 2025OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scalecited by 91
  9. 2025Time-MQA: Time Series Multi-Task Question Answering with Context Enhancementcited by 79
  10. 2024A Survey of Text-to-SQL in the Era of LLMs: Where are we, and where are we going?cited by 56
  11. 2025MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answeringcited by 53
  12. 2025Training-Free Group Relative Policy Optimizationcited by 53
10 more
  1. 2022A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directionscited by 51
  2. 2025XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQLcited by 40
  3. 2025ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Datasetcited by 36
  4. 2025Time Series Forecasting via Reasoning: A Slow-Thinking Approach with Reinforcement Fine-Tuned LLMscited by 34
  5. 2025OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Datacited by 33
  6. 2025CausalRivers -- Scaling up benchmarking of causal discovery for real-world time-seriescited by 25
  7. 2025A Survey of Reasoning and Agentic Systems in Time Series with Large Language Modelscited by 21
  8. 2025QuAnTS: Question Answering on Time Seriescited by 5
  9. 2017KV-match: A Subsequence Matching Approach Supporting Normalization and Time Warping [Extended Version]cited by 1
  10. 2025MS-Index: Fast Top-k Subsequence Search for Multivariate Time Series under Euclidean Distancecited by 1

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