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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2410.04047 |
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| _version_ | 1866910117590466560 |
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| author | Ye, Wen Yang, Wei Cao, Defu Zhang, Yizhou Tang, Lumingyuan Cai, Jie Liu, Yan |
| author_facet | Ye, Wen Yang, Wei Cao, Defu Zhang, Yizhou Tang, Lumingyuan Cai, Jie Liu, Yan |
| contents | Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning with domain-specific computational tools and an error feedback loop, TS-Reasoner enables domain-informed, constraint-aware analytical workflows that combine symbolic reasoning with precise numerical analysis. We assess the system's capabilities along two axes: (1) fundamental time series understanding assessed by TimeSeriesExam and (2) complex, multi-step inference evaluated by a newly proposed dataset designed to test both compositional reasoning and computational precision in time series analysis. Experiments show that our approach outperforms standalone general-purpose LLMs in both basic time series concept understanding as well as the multi-step time series inference task, highlighting the promise of domain-specialized agents for automating real-world time series reasoning and analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04047 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis Ye, Wen Yang, Wei Cao, Defu Zhang, Yizhou Tang, Lumingyuan Cai, Jie Liu, Yan Machine Learning Artificial Intelligence Time series analysis is crucial in real-world applications, yet traditional methods focus on isolated tasks only, and recent studies on time series reasoning remain limited to either single-step inference or are constrained to natural language answers. In this work, we introduce TS-Reasoner, a domain-specialized agent designed for multi-step time series inference. By integrating large language model (LLM) reasoning with domain-specific computational tools and an error feedback loop, TS-Reasoner enables domain-informed, constraint-aware analytical workflows that combine symbolic reasoning with precise numerical analysis. We assess the system's capabilities along two axes: (1) fundamental time series understanding assessed by TimeSeriesExam and (2) complex, multi-step inference evaluated by a newly proposed dataset designed to test both compositional reasoning and computational precision in time series analysis. Experiments show that our approach outperforms standalone general-purpose LLMs in both basic time series concept understanding as well as the multi-step time series inference task, highlighting the promise of domain-specialized agents for automating real-world time series reasoning and analysis. |
| title | TS-Reasoner: Domain-Oriented Time Series Inference Agents for Reasoning and Automated Analysis |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2410.04047 |