Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866914103460626432 |
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| author | Ning, Kanghui Pan, Zijie Jiang, Yushan Schneider, Anderson Nevmyvaka, Yuriy Song, Dongjin |
| author_facet | Ning, Kanghui Pan, Zijie Jiang, Yushan Schneider, Anderson Nevmyvaka, Yuriy Song, Dongjin |
| contents | Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding, structured multi-step reasoning, and faithful evaluation frameworks. The other advances system-level reasoning, moving beyond language-only explanations by incorporating multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. Together, these directions outline a flexible and extensible framework for advancing time series reasoning, aiming to deliver interpretable and trustworthy temporal intelligence across diverse domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16980 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision Ning, Kanghui Pan, Zijie Jiang, Yushan Schneider, Anderson Nevmyvaka, Yuriy Song, Dongjin Machine Learning Time series reasoning is emerging as the next frontier in temporal analysis, aiming to move beyond pattern recognition towards explicit, interpretable, and trustworthy inference. This paper presents a BlueSky vision built on two complementary directions. One builds robust foundations for time series reasoning, centered on comprehensive temporal understanding, structured multi-step reasoning, and faithful evaluation frameworks. The other advances system-level reasoning, moving beyond language-only explanations by incorporating multi-agent collaboration, multi-modal context, and retrieval-augmented approaches. Together, these directions outline a flexible and extensible framework for advancing time series reasoning, aiming to deliver interpretable and trustworthy temporal intelligence across diverse domains. |
| title | Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.16980 |