ATD: Dynamic Modeling for Extreme Event Prediction with Uncertainty Quantification with Multi-panel Time Series

ATD：通过多面板时间序列不确定性量化进行极端事件预测的动态建模

• 批准号: 2319260
• 负责人: Han Xiao
• 金额: $ 10万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319260&HistoricalAwards=false
• 关键词: ATD; Dynamic; Modeling; Extreme; Event

The ability to predict extreme geopolitical events is crucial for national security and foreign policy decision-making in a rapidly evolving world. A reliable and effective prediction system for this purpose must also provide a quantification of prediction uncertainty to support informed decision-making. In this project, the investigators aim to construct an advanced and comprehensive prediction system based on statistical models that interpret the dynamic relationship between multiple event series at multiple locations. The sophisticated statistical prediction system with quantified uncertainty has numerous real-world applications in various threat detection and risk assessment scenarios, such as cybersecurity, network activity monitoring, weather pattern forecasts, epidemics tracking, and others. The ability to effectively predict and assess potential threats will help organizations make informed decisions to ensure safety and security. This project will provide early-career students with valuable, interdisciplinary research experiences, offering a unique opportunity for growth and development. The investigators are committed to promoting diversity and inclusion in STEM fields, and will actively seek to recruit students from groups that are historically under-represented in science and engineering. A comprehensive project website will be created for project papers, reports, presentations, and links to relevant resources, providing a centralized repository of information and resources. The project’s outcomes, including the developed methods and software, will be widely disseminated for public use.The investigators will develop a dynamic matrix factor model for count series, which leverages a generalized linear factor model to reduce dimensionality and the dependence of the latent factor process to make forecasts. They also will develop a generalized matrix autoregressive model specifically tailored to low dimensional series (e.g., regional special events). The recently introduced repro-samples approach for irregular inference problems are used to assess model uncertainty. Given the complexity of the data, combining different models that better predict different parts of the data is beneficial. Two model averaging schemes are used by combining predictive distributions and dependent p-values, both of which come with uncertainty quantification. This leads to a warning system that predicts the likelihood of an extreme event occurring, as measured by a confidence level. The overall framework and methodology can be easily applied to other fields requiring prediction-based surveillance and monitoring. The resulting framework and methodology will have a profound impact on other fields of statistics, including high-dimensional statistics, statistical analysis of matrix and tensor data, modeling large panels of dependent data, statistical inference of irregular problems, predictive inference, fusion learning, and more. It will significantly advance statistics and data science research in general and provide new insights into data-driven decision making.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.

在快速发展的世界中，预测极端地缘政治事件的能力对于国家安全和外交政策决策至关重要。为此目的的可靠和有效的预测系统还必须提供预测不确定性的量化，以支持知情决策。在本项目中，研究者旨在构建一个基于统计模型的高级综合预测系统，以解释多个地点的多个事件序列之间的动态关系。具有量化不确定性的复杂统计预测系统在各种威胁检测和风险评估场景中有许多实际应用，例如网络安全，网络活动监测，天气模式预测，流行病跟踪等。有效预测和评估潜在威胁的能力将帮助组织做出明智的决策，以确保安全。该项目将为职业生涯早期的学生提供宝贵的跨学科研究经验，为他们的成长和发展提供独特的机会。研究人员致力于促进STEM领域的多样性和包容性，并将积极寻求从历史上在科学和工程领域代表性不足的群体中招募学生。一个综合性的项目网站将为项目论文、报告、演示文稿和相关资源的链接创建，提供一个集中的信息和资源储存库。该项目的成果，包括开发的方法和软件，将广泛传播，供公众使用。研究人员将开发一个计数序列的动态矩阵因子模型，该模型利用广义线性因子模型来降低潜在因子过程的维数和依赖性来进行预测。他们还将开发一个专门针对低维序列（例如，区域特殊事件）的广义矩阵自回归模型。最近引入的不规则推理问题的再现样本方法用于评估模型的不确定性。考虑到数据的复杂性，结合不同的模型来更好地预测数据的不同部分是有益的。结合预测分布和相关p值，使用了两种模型平均方案，这两种方案都带有不确定性量化。这就产生了一个预警系统，它可以预测极端事件发生的可能性，以置信度来衡量。总体框架和方法可以很容易地应用于需要基于预测的监测和监测的其他领域。由此产生的框架和方法将对统计的其他领域产生深远的影响，包括高维统计、矩阵和张量数据的统计分析、大型依赖数据面板的建模、不规则问题的统计推断、预测推断、融合学习等等。它将显著推进统计学和数据科学研究，并为数据驱动的决策提供新的见解。该奖项反映了美国国家科学基金会的法定使命，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。