Change Point Detection for Data with Network Structure

网络结构数据变点检测

基本信息

  • 批准号:
    2210358
  • 负责人:
  • 金额:
    $ 30万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2022
  • 资助国家:
    美国
  • 起止时间:
    2022-08-15 至 2023-10-31
  • 项目状态:
    已结题

项目摘要

Detecting breaks and anomalies in a mechanism that drives the generation of data represents a critical task, due to numerous applications in high-impact areas including health, social, and engineering sciences. This project aims to advance the state of the art of change point analysis for big and complex data, by developing a simple to implement, yet powerful, scalable algorithmic framework, thus providing new tools to examine high-dimensional, long streams for events of interest. The potential application domains of this project include but not limited to occurrence of seizure in brain connectivity data sets, coordinated market and other systemic failures in economic and finance data, and identification of orchestrated malicious activities in computer network streams. The developed algorithms and methodology will be implemented in open-source software, while curated data sets will be made available to the community for use in change point analysis investigations. The project will offer multiple unique opportunities for interdisciplinary research training of the future generation of statisticians and for further enhancement of diversity in mathematical sciences.To achieve the stated goals, the project (i) develops a unified detection framework for change points in complex statistical models for network and high dimensional time streams and (ii) provides a rigorous theoretical analysis of their accuracy in the form of consistency, finite sample bounds, and asymptotic distributions for the change points and other model parameters. The framework leverages a simple, easy to implement two-step strategy, wherein the first step one selects windows of the time series of appropriate length and using a standard exhaustive search strategy identifies at most a single change point in each of them. In the second step, a second search based on a global information criterion is employed to eliminate spurious change points. The strategy exhibits linear complexity in time (and thus matches the fastest available in the literature), yet is simple to implement and theoretically analyze, in particular for complex statistical models that exhibit network and low rank structure. Further, the following issues are rigorously addressed: (i) conditions of identifiability of the model parameters and the change points and (ii) probabilistic guarantees and uncertainty quantification for them in the presence of high dimensionality, network structure, temporal dependence, as well as dependence across data streams.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.
检测驱动数据生成的机制中的中断和异常是一项关键任务,因为在包括健康,社会和工程科学在内的高影响力领域有许多应用。该项目旨在通过开发一个易于实现但功能强大的可扩展算法框架来推进大数据和复杂数据的变化点分析技术,从而提供新的工具来检查感兴趣的事件的高维长流。该项目的潜在应用领域包括但不限于大脑连接数据集中的癫痫发作,经济和金融数据中的协调市场和其他系统性故障,以及计算机网络流中精心策划的恶意活动的识别。所开发的算法和方法将在开放源代码软件中实施,而精选的数据集将提供给社区用于变化点分析调查。该项目将为跨学科研究培训未来一代统计人员和进一步加强数学科学的多样性提供多种独特的机会。该项目(i)为网络和高维时间流的复杂统计模型中的变化点开发统一的检测框架,以及(ii)提供了一个严格的理论分析,其准确性的形式一致性,有限样本界,渐近分布的变化点和其他模型参数。该框架利用简单的、易于实现的两步策略,其中第一步选择适当长度的时间序列的窗口,并且使用标准的穷举搜索策略来识别它们中的每一个中的至多单个变化点。在第二步中,采用基于全局信息准则的第二次搜索来消除伪变点。该策略在时间上表现出线性复杂性(因此与文献中最快的方法相匹配),但易于实现和理论分析,特别是对于表现出网络和低秩结构的复杂统计模型。此外,还严格处理了以下问题:(i)模型参数和变点的可识别性条件,以及(ii)在高维、网络结构、时间依赖性存在的情况下,它们的概率保证和不确定性量化,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。

项目成果

期刊论文数量(3)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Inference on the Change Point under a High Dimensional Covariance Shift
  • DOI:
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    A. Kaul;Hongjin Zhang;K. Tsampourakis;G. Michailidis
  • 通讯作者:
    A. Kaul;Hongjin Zhang;K. Tsampourakis;G. Michailidis
Multiple Change Point Detection in Reduced Rank High Dimensional Vector Autoregressive Models
  • DOI:
    10.1080/01621459.2022.2079514
  • 发表时间:
    2021-09
  • 期刊:
  • 影响因子:
    3.7
  • 作者:
    Peiliang Bai;Abolfazl Safikhani;G. Michailidis
  • 通讯作者:
    Peiliang Bai;Abolfazl Safikhani;G. Michailidis
Challenges for Anomaly Detection in Large-Scale Cyber-Physical Systems
大规模信息物理系统中异常检测的挑战
  • DOI:
    10.1162/99608f92.7b8b6a89
  • 发表时间:
    2023
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Michailidis, George
  • 通讯作者:
    Michailidis, George
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George Michailidis其他文献

Asymptotics for <math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si4.gif" display="inline" overflow="scroll" class="math"><mi>p</mi></math>-value based threshold estimation under repeated measurements
  • DOI:
    10.1016/j.jspi.2016.01.009
  • 发表时间:
    2016-07-01
  • 期刊:
  • 影响因子:
  • 作者:
    Atul Mallik;Bodhisattva Sen;Moulinath Banerjee;George Michailidis
  • 通讯作者:
    George Michailidis
Queueing Networks of Random Link Topology: Stationary Dynamics of Maximal Throughput Schedules
  • DOI:
    10.1007/s11134-005-0858-x
  • 发表时间:
    2005-05-01
  • 期刊:
  • 影响因子:
    0.700
  • 作者:
    Nicholas Bambos;George Michailidis
  • 通讯作者:
    George Michailidis
DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data
  • DOI:
    10.1186/s12859-024-05994-1
  • 发表时间:
    2024-12-18
  • 期刊:
  • 影响因子:
    3.300
  • 作者:
    Christopher Patsalis;Gayatri Iyer;Marci Brandenburg;Alla Karnovsky;George Michailidis
  • 通讯作者:
    George Michailidis
Statistica Sinica Preprint No: SS-2022-0323
《统计》预印本编号:SS-2022-0323
  • DOI:
  • 发表时间:
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Abhishek Kaul;George Michailidis;Statistica Sinica
  • 通讯作者:
    Statistica Sinica
Preface: Computational biomedicine
  • DOI:
    10.1007/s10479-018-3116-4
  • 发表时间:
    2019-01-14
  • 期刊:
  • 影响因子:
    4.500
  • 作者:
    Anton Kocheturov;Panos Pardalos;George Michailidis
  • 通讯作者:
    George Michailidis

George Michailidis的其他文献

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{{ truncateString('George Michailidis', 18)}}的其他基金

ATD: Spatio-Temporal Modeling for Identifying Changes in Land Use
ATD:识别土地利用变化的时空模型
  • 批准号:
    2334735
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Change Point Detection for Data with Network Structure
网络结构数据变点检测
  • 批准号:
    2348640
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: ATD: Geospatial Modeling and Risk Mitigation for Human Movement Dynamics under Hurricane Threats
合作研究:ATD:飓风威胁下人类运动动力学的地理空间建​​模和风险缓解
  • 批准号:
    2319552
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: IMR: MM-1A: Scalable Statistical Methodology for Performance Monitoring, Anomaly Identification, and Mapping Network Accessibility from Active Measurements
合作研究:IMR:MM-1A:用于性能监控、异常识别和主动测量映射网络可访问性的可扩展统计方法
  • 批准号:
    2319593
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
ATD: Spatio-Temporal Modeling for Identifying Changes in Land Use
ATD:识别土地利用变化的时空模型
  • 批准号:
    2124507
  • 财政年份:
    2021
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
CDS&E: Statistical Methodology for Analysis and Forecasting with Large Scale Temporal Data
CDS
  • 批准号:
    1821220
  • 财政年份:
    2018
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
ATD: Collaborative Research: Extremal Dependence and Change-Point Detection Methods for High-Dimensional Data Streams with Applications to Network Cybersecurity
ATD:协作研究:高维数据流的极端依赖性和变点检测方法及其在网络网络安全中的应用
  • 批准号:
    1830175
  • 财政年份:
    2018
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
BIGDATA: Collaborative Research: IA: F: Too Interconnected to Fail? Network Analytics on Complex Economic Data Streams for Monitoring Financial Stability
BIGDATA:协作研究:IA:F:互联性太强以至于不会失败?
  • 批准号:
    1632730
  • 财政年份:
    2016
  • 资助金额:
    $ 30万
  • 项目类别:
    Continuing Grant
CyberSEES: Type 2: Collaborative Research: Tenable Power Distribution Networks
Cyber​​SEES:类型 2:协作研究:可维持的配电网络
  • 批准号:
    1540093
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
Collaborative Research: Statistical Methodology for Network based Integrative Analysis of Omics Data
合作研究:基于网络的组学数据综合分析统计方法
  • 批准号:
    1545277
  • 财政年份:
    2015
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant

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解大型非对称鞍点(Saddle Point) 问题的有效算法的研究
  • 批准号:
    60573157
  • 批准年份:
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Change Point Detection for Data with Network Structure
网络结构数据变点检测
  • 批准号:
    2348640
  • 财政年份:
    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Standard Grant
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利用矩阵分解进行快速在线变化点检测
  • 批准号:
    2872651
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    2023
  • 资助金额:
    $ 30万
  • 项目类别:
    Studentship
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DMS-EPSRC: Change Point Detection and Localization in High-Dimensions: Theory and Methods
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    EP/V013432/1
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  • 批准号:
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  • 财政年份:
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