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S

Shirui Pan

Griffith University

M

Ming Jin

Griffith University

Z

Zhao Tan

Griffith University

Y

Yiji Zhao

Yunnan University

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References (22)

[1]
QuAnTS: Question Answering on Time Series
2025Felix Divo, Maurice Kraus et al.
[2]
Training-Free Group Relative Policy Optimization
2025Yuzheng Cai, Siqi Cai et al.
[3]
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models
2025Ching Chang, Yidan Shi et al.
[4]
XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
2025Yifu Liu, Yin Zhu et al.
[5]
ITFormer: Bridging Time Series and Natural Language for Multi-Modal QA with Large-Scale Multitask Dataset
2025Yilin Wang, Peixuan Lei et al.
[6]
Time Series Forecasting as Reasoning: A Slow-Thinking Approach with Reinforced LLMs
2025Yucong Luo, Yitong Zhou et al.
[7]
CausalRivers - Scaling up benchmarking of causal discovery for real-world time-series
2025Gideon Stein, Maha Shadaydeh et al.
[8]
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
2025Jialin Chen, Aosong Feng et al.
[9]
OmniSQL: Synthesizing High-quality Text-to-SQL Data at Scale
2025Haoyang Li, Shang Wu et al.
[10]
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement
2025Yaxuan Kong, Yiyuan Yang et al.
[11]
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
2024Zhe Xie, Zeyan Li et al.
[12]
CHASE-SQL: Multi-Path Reasoning and Preference Optimized Candidate Selection in Text-to-SQL
2024Mohammadreza Pourreza, Hailong Li et al.
[13]
A Survey of Text-to-SQL in the Era of LLMs: Where Are We, and Where Are We Going?
2024Xinyu Liu, Shuyu Shen et al.
[14]
CHESS: Contextual Harnessing for Efficient SQL Synthesis
2024Shayan Talaei, Mohammadreza Pourreza et al.
[15]
Enhancing Text-to-SQL Capabilities of Large Language Models through Tailored Promptings
2024Zhao Tan, X. Liu et al.
[16]
MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL
2023Bing Wang, Changyu Ren et al.
[17]
Time-LLM: Time Series Forecasting by Reprogramming Large Language Models
2023Ming Jin, Shiyu Wang et al.
[18]
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
2023Jinyang Li, Binyuan Hui et al.
[19]
DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction
2023M. Pourreza, Davood Rafiei
[20]
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
2022Bowen Qin, Binyuan Hui et al.

Showing 20 of 22 references

Founder's Pitch

"Neuro-symbolic framework for intuitive natural language querying of time series databases with significant scalability improvements."

Natural Language Querying for DatabasesScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

2/4 signals

5

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Why It Matters

This research addresses a critical usability gap in accessing insights from time series databases which are extensive in various industries. Without such advancements, non-expert users face significant difficulty in extracting meaningful patterns and trends from large datasets.

Product Angle

The product could be a user-friendly dashboard or API that interprets and processes natural language queries into data insights, suitable for firms managing large amounts of time-series data and lacking the resources to handle specialized querying internally.

Disruption

It could replace existing SQL querying methods and text-to-SQL tools that are not optimized for handling time-series data, especially for non-experts.

Product Opportunity

The market for time series databases is rapidly growing across sectors such as finance, IoT, and energy. Companies in these sectors have large datasets and a demand for intuitive data querying tools; they would pay for tools that make this process efficient and accessible.

Use Case Idea

Develop a service that allows operations teams in industries like finance or IoT to input complex natural language queries and receive insights from their time-series databases without needing SQL expertise.

Science

Sonar-TS proposes a 'Search-Then-Verify' pipeline for querying time series databases using natural language. It employs SQL to identify candidate data windows and uses Python programs to further verify these against user queries, bypassing the limitations of existing Text-to-SQL and time series models that struggle with complex morphological intents and large datasets.

Method & Eval

Sonar-TS was tested using the NLQTSBench, a benchmarking suite that includes four levels of complexity to evaluate querying precision, pattern recognition, semantic reasoning, and insight synthesis. It outperforms traditional methods by effectively navigating complex queries over large, segmented time series data.

Caveats

The framework could face challenges in real-world usability if the natural language processing aspect does not accurately interpret user intents. Additionally, initial adoption might be slow if users are accustomed to traditional querying methods.

Author Intelligence

Shirui Pan

LEAD
Griffith University
s.pan@griffith.edu.au

Ming Jin

LEAD
Griffith University
ming.jin@griffith.edu.au

Zhao Tan

Griffith University

Yiji Zhao

Yunnan University

Shiyu Wang

Chang Xu

Microsoft Research Asia

Yuxuan Liang

The Hong Kong University of Science and Technology (Guangzhou)

Xiping Liu

Jiangxi University of Finance and Economics