III: Small: Uncovering the Myths of Unlikelihood: Granger Graphical Models for Anomaly Detection in Multivariate Time-Series Data

III：小：揭开不可能的神话：多元时间序列数据中异常检测的格兰杰图形模型

• 批准号: 1117740
• 负责人: Yan Liu
• 金额: $ 50万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117740&HistoricalAwards=false
• 关键词: III; Small; Uncovering; Myths; Unlikelihood

The project aims to develop effective approaches to anomaly detection from high-dimensional time series data, motivated by applications such as oil drilling, semiconductor fabrication, and railroad operation.The proposed approach takes advantage of Granger Graphical models, which uncover the temporal dependencies between variables, to efficiently compute a robust correlation anomaly score for each variable and obtain insights regarding the causes of anomalies. The project develops effective approaches to addresses several specific challenges that arise in real-world applications of anomaly detection, including (1) nonlinear temporal dependencies; (2) hidden variables; and (3) massive amounts of data. The resulting algorithms will be evaluated on two real production systems: an oil-field mechanical system and a semi-conductor fabrication system.The project is expected to advance the state of the art in anomaly detection for high-dimensional time series data that arise in many application domains. It offers research-based training opportunities at the intersection of machine learning, data mining, and intelligent production management, as well as operational research in general.Workshops and mini-courses will be organized to introduce advanced machine learning techniques to students, practitioners, and researchers in production management. The anomaly detection code and data sets will be freely disseminated to the broader research and educational community. Additional information about the project can be found at: http://www-bcf.usc.edu/~liu32/ggm.htm.

该项目旨在开发有效的方法来从高维时间序列数据中检测异常，如石油钻井，半导体制造和铁路运营的应用，所提出的方法利用格兰杰图形模型，揭示变量之间的时间依赖关系，有效地计算每个变量的鲁棒相关异常得分，并获得有关异常原因的见解。该项目开发了有效的方法来解决异常检测的实际应用中出现的几个具体挑战，包括（1）非线性时间依赖性;（2）隐藏变量;（3）大量数据。该算法将在两个真实的生产系统上进行评估：一个油田机械系统和一个半导体制造系统。该项目预计将推进在许多应用领域出现的高维时间序列数据的异常检测的最新技术。它提供基于研究的培训机会，在机器学习，数据挖掘和智能生产管理的交叉点，以及一般的运筹学。研讨会和迷你课程将组织介绍先进的机器学习技术的学生，从业者和生产管理的研究人员。异常检测代码和数据集将免费分发给更广泛的研究和教育界。有关该项目的更多信息，请访问：http://www-bcf.usc.edu/~liu32/ggm.htm。