ATD: Efficient and Effective Algorithms for Detection of Anomalies in High-dimensional Spatiotemporal Data with Large Amounts of Missing Data

ATD：高效且有效的高维时空数据异常检测算法

• 批准号: 2318925
• 负责人: Hsin-Hsiung Huang
• 金额: $ 10万
• 依托单位: The University of Central Florida Board of Trustees
• 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: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2318925&HistoricalAwards=false
• 关键词: ATD; Efficient; Effective; Algorithms; Detection

Detecting rare events of anomalies using high-dimensional real-world data with mixed-type multivariate response, sparse and unknown signals and a large number of missing values is very challenging. The architectural integration of domain knowledge and data with spatial and temporal variables significantly improves the model reliability, interpretability, and prediction accuracy. For predicting anomalies, the investigator plans to develop efficient and effective posterior sampling algorithms and apply these methods to various real-word data with interdisciplinary collaborations. Students, including those from underrepresented groups, will be involved in education and outreach activities featuring real world problem-solving.The research aims to develop novel anomaly detection algorithms. These will leverage hybrid information from the domain knowledge (represented as prior distributions) and regularization constraints to establish an efficient and effective search direction. Data information embedded in likelihood functions and regression models will reduce the search space for parameter estimation.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.

利用具有混合型多元响应、稀疏未知信号和大量缺失值的高维真实数据检测罕见异常事件是非常具有挑战性的。领域知识和数据与时空变量的体系结构集成显著提高了模型的可靠性、可解释性和预测精度。为了预测异常，研究者计划开发高效的后验采样算法，并通过跨学科合作将这些方法应用于各种实际数据。学生，包括来自代表性不足群体的学生，将参与以解决现实世界问题为特色的教育和拓展活动。本研究旨在开发新的异常检测算法。这将利用来自领域知识（表示为先验分布）和正则化约束的混合信息来建立高效和有效的搜索方向。嵌入在似然函数和回归模型中的数据信息将减少参数估计的搜索空间。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。