用提示调优让单变量模型学会处理多变量医疗时间序列
Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series
- 通过提示调优技术，让冻结的单变量模型适应多变量数据
- 在两个MIMIC分类任务和流感预测上优于多个基线方法
- 适合缺乏标注数据的医疗时序分析场景
时间序列基础模型在大规模数据上预训练，可实现多样化任务的最先进性能。然而，目前关于其在医疗应用中的表现研究有限，尤其在标注数据稀缺的情况下。我们发现现有时间序列基础模型多数为单变量或假设通道独立，即虽能处理多变量时间序列，但未建模变量间的关联。本文提出一种受提示调优启发的微调方法——广义提示调优（Gen-P-Tuning），使现有的单变量时间序列基础模型（保持冻结）能够用于多变量时间序列预测。该方法实现了多变量时间序列中不同通道信息的融合。我们在两个MIMIC分类任务和流感样疾病预测任务上验证了该方法的有效性，结果优于多种基线模型。
原文摘要 · Abstract (English)
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.
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