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arXiv · 2609.34783

TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query over Time-Series Databases

Fei Lyu·Zhiyi Peng·Jiaming Liu·Yixuan Yang·Changjian Chen·Zhuo Tang·Jiapeng Zhang·Kenli Li
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Abstract

Large language models (LLMs) have significantly advanced natural language querying over relational databases, yet their ability to query time-series databases (TSDBs) remains largely unassessed. Existing benchmarks fail to adequately capture the non-unified query syntaxes, diverse application domains, and unique time-specific query intents inherent to TSDBs. To address this gap, we introduce TQTS-BENCH, a multi-syntax benchmark for evaluating text-to-query capabilities over TSDBs. TQTS-BENCH contains 6,125 high-quality question-answering (QA) pairs spanning 97 TSDBs, 23 distinct query syntaxes, 22 application domains, and 4 types of time-specific query intents. It is constructed through a human-centric AI-assisted workflow, where all QA pairs are carefully reviewed and revised by domain experts to ensure quality and correctness. Extensive evaluations of advanced LLMs and state-of-the-art text-to-query methods reveal challenges in querying TSDBs. Even the best-performing model evaluated, Claude-Opus-5, achieves only 48.98% execution accuracy, while humans reach 87.34%. Error analysis reveals that this performance gap mainly stems from the heterogeneous query syntaxes across different TSDBs, misinterpretation of time-specific intents, and incorrect schema linking. These findings highlight new opportunities to narrow the gap between current LLM capabilities and the requirements of TSDB queries in real-world applications. The benchmark is available at: https://anonymous.4open.science/r/TQTS-Bench-00CD.

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Fei Lyu, Zhiyi Peng, Jiaming Liu, Yixuan Yang, Changjian Chen, Zhuo Tang, Jiapeng Zhang, Kenli Li. 2026-09-28. TQTS-Bench: A Multi-Syntax Benchmark for Text-to-Query over Time-Series Databases. https://arxiv.org/abs/2609.34783

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