Summary of Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models For Multivariate Healthcare Time Series, by Mingzhu Liu et al.
Generalized Prompt Tuning: Adapting Frozen Univariate Time Series Foundation Models for Multivariate Healthcare Time Series
by Mingzhu Liu, Angela H. Chen, George H. Chen
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Generalized Prompt Tuning (Gen-P-Tuning), a novel technique for adapting pre-trained univariate time series foundation models to handle multivariate time series prediction. The authors fine-tune an existing model using prompt-tuning-inspired methods, enabling the combination of information across channels in multivariate time series. The approach is demonstrated on two MIMIC classification tasks and influenza-like illness forecasting, outperforming various baselines. The study aims to bridge the gap between state-of-the-art performance in diverse tasks and medical applications, where labeled data can be scarce. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores ways to improve time series predictions by using pre-trained models that are already good at making predictions. However, these models often don’t work well when dealing with multiple related variables (like blood pressure and temperature). The authors develop a new way to adapt these models for use in medical applications where there isn’t much labeled data available. They test their approach on two tasks: diagnosing patient conditions and forecasting illnesses like the flu. Their method does better than previous approaches, which is important because making accurate predictions can help doctors make better decisions. |
Keywords
» Artificial intelligence » Classification » Prompt » Temperature » Time series
