{"ID":2853238,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16980","arxiv_id":"2510.16980","title":"Towards Interpretable and Trustworthy Time Series Reasoning: A BlueSky Vision","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.16980","url_pdf":"https://arxiv.org/pdf/2510.16980v1","authors":"[\"Kanghui Ning\",\"Zijie Pan\",\"Yushan Jiang\",\"Anderson Schneider\",\"Yuriy Nevmyvaka\",\"Dongjin Song\"]","published":"2025-10-19T19:48:15Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[]","has_code":false}