Sonar-TS: Search-Then-Verify Natural Language Querying for Time Series Databases
arXiv:2602.17001v1 Announce Type: new 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
arXiv:2602.17001v1 Announce Type: new 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.
Executive Summary
This article proposes Sonar-TS, a neuro-symbolic framework for Natural Language Querying for Time Series Databases (NLQ4TSDB). The framework utilizes a Search-Then-Verify pipeline to tackle complex temporal queries, addressing challenges faced by existing Text-to-SQL methods and time series models. The authors introduce NLQTSBench, a large-scale benchmark for evaluating NLQ over TSDB-scale histories. Experiments demonstrate Sonar-TS's effectiveness in navigating complex temporal queries, presenting a general framework and evaluation standard for future research. The proposed framework has the potential to significantly improve the retrieval of meaningful events, intervals, and summaries from massive temporal records.
Key Points
- ▸ Sonar-TS introduces a Search-Then-Verify pipeline for NLQ4TSDB, addressing challenges faced by existing methods
- ▸ The framework utilizes a feature index to ping candidate windows via SQL, followed by generated Python programs to verify candidates
- ▸ NLQTSBench is introduced as a large-scale benchmark for evaluating NLQ over TSDB-scale histories
Merits
Effective navigation of complex temporal queries
Sonar-TS demonstrates effectiveness in handling queries where traditional methods fail, highlighting its potential to improve NLQ4TSDB performance
General framework and evaluation standard
The proposed framework and benchmark provide a foundation for future research in NLQ4TSDB, enabling systematic study and comparison of methods
Addressing ultra-long histories challenge
Sonar-TS tackles the challenge of handling ultra-long histories, enabling effective retrieval of meaningful events and summaries from massive temporal records
Demerits
Limited evaluation of traditional methods
The article primarily focuses on the performance of Sonar-TS, with limited comparison to traditional methods, which may limit the generalizability of the results
Potential for overfitting
The use of a Search-Then-Verify pipeline may lead to overfitting, particularly if the generated Python programs are not robust to variations in input queries or data
Scalability and efficiency concerns
The framework's scalability and efficiency may be impacted by the complexity of the temporal queries and the size of the dataset, which could affect its practicality for large-scale applications
Expert Commentary
The article presents a significant contribution to the field of Natural Language Querying for Time Series Databases, addressing critical challenges faced by existing methods. The proposed Sonar-TS framework demonstrates effectiveness in navigating complex temporal queries and provides a general framework and evaluation standard for future research. However, the article could benefit from a more comprehensive evaluation of traditional methods and a deeper discussion of the potential for overfitting and scalability concerns. Nevertheless, the proposed framework has the potential to revolutionize the field of NLQ4TSDB, enabling more effective retrieval of meaningful events, intervals, and summaries from massive temporal records.
Recommendations
- ✓ Future research should focus on evaluating the performance of Sonar-TS in more realistic scenarios, considering variations in input queries and data
- ✓ The authors should provide more detailed information on the implementation and scalability of the Search-Then-Verify pipeline, addressing concerns related to overfitting and efficiency
