研究人员正在探索将表格基础模型（TFMs）应用于复杂的时间序列预测任务，特别是在预后与健康管理（PHM）和生存分析领域。这些模型通过上下文学习或特定预训练等方法适配时间序列数据，有望高效处理碎片化和审查数据。初步结果表明，TFMs 在低数据量场景下，其表现可能优于传统的序列模型甚至专门的生存分析技术。 AI
影响 将基础模型的能力扩展到审查时间序列数据，可能改进预测性维护和医疗保健分析。
排序理由 多篇arXiv论文介绍了将表格基础模型应用于生存分析和预后等时间序列预测任务的新方法。
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arXiv:2606.05481v1 Announce Type: new Abstract: Data-driven Prognostics and Health Management (PHM) uses time-varying condition-monitoring data to diagnose system states and estimate remaining useful life in engineered assets. These tasks are central to maintenance planning, but …
arXiv:2606.04564v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have made rapid progress in standard classification and regression, but time-to-event survival prediction tasks have remained largely untouched. Unlike in standard regression tasks, survival predicti…
arXiv:2606.03689v1 Announce Type: cross Abstract: Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs. Widely used in several domains, including healthcare and churn prediction, a central challenge in its applicability s…
Survival Analysis (SA) is a statistical framework that models the time span until some event of interest occurs. Widely used in several domains, including healthcare and churn prediction, a central challenge in its applicability stems from the time of the event being partially ob…