CRII: III: Rare Event Prediction in Time Series

CRII：III：时间序列中的罕见事件预测

• 批准号: 2104270
• 负责人: Juhua Hu
• 金额: $ 17.49万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2104270&HistoricalAwards=false
• 关键词: CRII; III; Rare; Event; Prediction

Predicting rare events within a time series is a critical task in many real-world applications. Taking the healthcare industry as an example, it would be very useful to predict an influx in the number of patients that would overwhelm a hospital beyond its capacity. Without such capability, it could have negative consequences in the hospital’s ability to adequate health care. Rare event prediction in time series is a challenging problem due to the non-linear nature of rare events, the inability to capture their key information in the systematic components of the temporal data, and the data imbalance between rare and normal events. This project will result in a framework to address these challenges. With such a prediction framework, steps can be taken to ensure the affected population's readiness for such rare events. For instance, the application of the resulting technology will significantly improve readiness in the healthcare care industry. Furthermore, the resulting technology can be applied to other areas of societal interest.This project studies the nature of rare events in time series and addresses the above-mentioned challenges with three technical aims. First, it is well known that sequence models such as recurrent neural networks can be used to capture both the temporal and non-linear nature of the data. Nevertheless, they have not been well studied for the problem of rare event prediction in time series. One goal of this project is to thoroughly study if sequence models can capture both the temporal and non-linear nature of the rare events to facilitate the prediction. Second, the key information of rare events can be hidden by the seasonal fluctuations in the time series. Another goal of this project is to study if time series decomposition that separates the seasonality can help the prediction of rare events. Finally, in terms of the data imbalance problem, traditional prediction models often treat normal and rare events equivalently, which can be harmful for the rare event prediction. This project will develop a new method based on the properties of rare events to emphasize their importance. Moreover, filters can be specifically designed to extract rare events better to reduce the effect from large amount of normal data. Both strategies have not been well studied for the rare event prediction in time series, which will be the third goal of this project.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

预测时间序列中的罕见事件是许多现实世界应用中的关键任务。以医疗保健行业为例，预测患者数量的涌入将使医院不堪重负。如果没有这种能力，可能会对医院提供适当保健的能力产生负面影响。时间序列中的稀有事件预测是一个具有挑战性的问题，由于稀有事件的非线性性质，无法捕捉其关键信息的时间数据的系统组成部分，以及罕见和正常事件之间的数据不平衡。该项目将产生一个应对这些挑战的框架。有了这样一个预测框架，就可以采取措施，确保受影响人口为这种罕见事件做好准备。例如，由此产生的技术的应用将显着提高医疗保健行业的准备程度。此外，由此产生的技术可以应用于其他社会感兴趣的领域。该项目研究时间序列中罕见事件的性质，并以三个技术目标应对上述挑战。首先，众所周知，诸如递归神经网络的序列模型可以用于捕获数据的时间和非线性性质。然而，对于时间序列中的稀有事件预测问题，它们还没有得到很好的研究。该项目的一个目标是深入研究序列模型是否可以捕获罕见事件的时间和非线性性质，以促进预测。其次，罕见事件的关键信息可以被时间序列中的季节波动所隐藏。本计画的另一个目标是研究分离季节性的时间序列分解是否有助于预测罕见事件。最后，在数据不平衡问题上，传统的预测模型往往将正常事件和罕见事件等同对待，这对罕见事件的预测是不利的。该项目将开发一种基于稀有事件属性的新方法，以强调其重要性。此外，可以专门设计过滤器来更好地提取罕见事件，以减少大量正常数据的影响。这两种策略在时间序列中的罕见事件预测方面都没有得到很好的研究，这将是该项目的第三个目标。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Sub-Sequence Graph Representation Learning on High Variability Data for Dynamic Risk Prediction in Critical Care

• DOI: 10.1109/bigdata55660.2022.10020963
• 发表时间: 2022-12
• 期刊: 2022 IEEE International Conference on Big Data (Big Data)
• 作者: Ankur Teredesai;Sijin Huang;Tucker Stewart;Juhua Hu;Armaan Thakker;Katherine Stern;G. O’Keefe