广义提示微调：将冻结的单变量时间序列基础模型适配于多变量医疗时间序列
Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series
AI总结：
本文提出广义提示微调（Gen-P-Tuning），在冻结单变量时间序列基础模型的前提下，通过跨通道信息融合适配多变量医疗时间序列预测，并在MIMIC分类和流感样疾病预测任务上验证其有效性。
摘要：
时间序列基础模型在大规模数据集上进行预训练，并能够在多种任务中取得最先进的性能。然而，迄今为止，展示这些模型在标注数据可能稀缺的医学应用中表现如何的工作仍然有限。此外，我们观察到，目前大多数时间序列基础模型要么本质上是单变量的，要么假设通道独立，这意味着它们处理多变量时间序列，但并不对不同变量之间的相互关系进行建模。在本文中，我们提出了一种受提示微调启发的微调技术，即广义提示微调（Gen-P-Tuning），它使我们能够适配现有的单变量时间序列基础模型（视为冻结），以处理多变量时间序列预测。我们的方法提供了一种结合多变量时间序列各通道（变量）信息的方式。我们在两个MIMIC分类任务以及流感样疾病预测上，针对各种基线展示了我们微调方法的有效性。
英文摘要：
Time series foundation models are pre-trained on large datasets and are able to achieve state-of-the-art performance in diverse tasks. However, to date, there has been limited work demonstrating how well these models perform in medical applications, where labeled data can be scarce. Further, we observe that currently, the majority of time series foundation models either are univariate in nature, or assume channel independence, meaning that they handle multivariate time series but do not model how the different variables relate. In this paper, we propose a prompt-tuning-inspired fine-tuning technique, Generalized Prompt Tuning (Gen-P-Tuning), that enables us to adapt an existing univariate time series foundation model (treated as frozen) to handle multivariate time series prediction. Our approach provides a way to combine information across channels (variables) of multivariate time series. We demonstrate the effectiveness of our fine-tuning approach against various baselines on two MIMIC classification tasks, and on influenza-like illness forecasting.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
