Nonlinear dynamic factor models and dynamic factor driven functional time series models

非线性动态因子模型和动态因子驱动的函数时间序列模型

• 批准号: 1513409
• 负责人: Rong Chen
• 金额: $ 23万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2019-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513409&HistoricalAwards=false
• 关键词: Nonlinear; dynamic; factor; models; driven

Time series analysis comprises methods that allow for the discovery of dependent and dynamic structures in observations taken over time and that provide accurate predictions of the future. Time series data occur in many important application fields including economics, finance, environmental studies, neuroscience, ecology, and meteorology. In this age of Big Data, with advanced data collection capability, researchers routinely encounter large panels of time series data. How to effectively analyze the common dynamic feature of these time series, how to discover their interconnection, how to make accurate predictions, and how to assess the overall risk are important questions. The project aims to answer these questions by investigating statistical methods that extract common features from a large number of time series. The project will also describe methods to analyze data in the form of curves or images observed over time. This project provides advanced data analysis tools for solving many real world problems, and paves the way for developing a new research area in statistics. The project includes activities related to education and research training of graduate and undergraduate students, and plans for recruiting women and underrepresented minority students into the field of statistics. Results will be disseminated through conference presentations, publications, and distribution of software. The project focuses on two closely related topics: (i) developing a class of nonlinear dynamic factor models, along with associated statistical inference procedures and derivation of the theoretical properties of the proposed estimators; and (ii) developing an efficient nonparametric inference procedure for functional time series based on dimension reduction using dynamic factor models. Modeling and analyzing high-dimensional time series requires efficient dimensional reduction tools, with factor models being one of the most commonly used techniques. The project extends standard linear dynamic factor models to nonlinear models in order to capture the nonlinearity often encountered in practice. When functional or distributional observations are observed over time and exhibit dynamic behaviors, time series models in the functional space become a necessary and useful tool for analyzing such data, as well as making predictions of the future. New nonparametric approaches to modeling functional time series utilizing factor models as a dimension reduction tool will be developed. The two research topics are rapidly gaining importance as more and more applications involve such types of data. The combination of these two closely related projects builds a comprehensive framework for modern time series analysis. For each project, statistical properties of the underlying models, statistical inference and predictions for these models, and theoretical properties of the inferential and prediction methods will be studied.

时间序列分析包括允许在随时间推移进行的观测中发现相关和动态结构并提供对未来的准确预测的方法。 时间序列数据出现在许多重要的应用领域，包括经济学，金融，环境研究，神经科学，生态学和气象学。 在这个大数据时代，随着先进的数据收集能力，研究人员经常会遇到大量的时间序列数据。 如何有效地分析这些时间序列的共同动态特征，发现它们之间的内在联系，做出准确的预测，并对整体风险进行评估，是一个重要的问题。 该项目旨在通过调查从大量时间序列中提取共同特征的统计方法来回答这些问题。 该项目还将描述分析数据的方法，这些数据以曲线或图像的形式随着时间的推移而观察到。 该项目为解决许多真实的世界问题提供了先进的数据分析工具，并为开发统计学的新研究领域铺平了道路。 该项目包括与研究生和本科生的教育和研究培训有关的活动，以及招聘妇女和代表性不足的少数民族学生进入统计领域的计划。 将通过会议介绍、出版物和软件分发来传播成果。该项目的重点是两个密切相关的主题：（i）开发一类非线性动态因子模型，沿着相关的统计推断程序和推导的理论性质的建议估计;和（ii）开发一个有效的非参数推断程序的功能时间序列的基础上减少使用动态因子模型。建模和分析高维时间序列需要有效的降维工具，因子模型是最常用的技术之一。该项目将标准的线性动态因子模型扩展到非线性模型，以捕捉实践中经常遇到的非线性。当函数或分布观测随着时间的推移而被观察到并表现出动态行为时，函数空间中的时间序列模型就成为分析此类数据以及预测未来的必要和有用的工具。 新的非参数方法建模功能的时间序列利用因素模型作为降维工具将开发。 随着越来越多的应用程序涉及此类数据，这两个研究主题的重要性正在迅速增加。 这两个密切相关的项目的结合为现代时间序列分析构建了一个全面的框架。对于每个项目，将研究基础模型的统计特性，这些模型的统计推断和预测，以及推断和预测方法的理论特性。

项目成果

NTS: An R Package for Nonlinear Time Series Analysis

NTS：用于非线性时间序列分析的 R 包

• DOI: 10.32614/rj-2021-016
• 发表时间: 2020
• 期刊: The R Journal
• 作者: Liu, Xialu;Chen, Rong;Tsay, Ruey
• 通讯作者: Tsay, Ruey

Factor models for matrix-valued high-dimensional time series

• DOI: 10.1016/j.jeconom.2018.09.013
• 发表时间: 2019-01-01
• 期刊: JOURNAL OF ECONOMETRICS
• 影响因子: 6.3
• 作者: Wang, Dong;Liu, Xialu;Chen, Rong
• 通讯作者: Chen, Rong

Threshold factor models for high-dimensional time series

• DOI: 10.1016/j.jeconom.2020.01.005
• 发表时间: 2018-09
• 期刊: Journal of Econometrics
• 影响因子: 6.3
• 作者: Xialu Liu;Rong Chen

iGroup Learning and iDetect for Dynamic Anomaly Detection with Applications in Maritime Threat Detection

iGroup Learning 和 iDetect 用于动态异常检测及其在海上威胁检测中的应用

• DOI: 10.1109/ths.2018.8574162
• 发表时间: 2018
• 期刊: 2018 IEEE International Symposium on Technologies for Homeland Security (HST
• 作者: Cai, Chencheng;Chen, Rong;Liu, Alexander D.;Roberts, Fred S.;Xie, Minge
• 通讯作者: Xie, Minge

Dirichlet ARMA models for compositional time series

用于组合时间序列的 Dirichlet ARMA 模型

• DOI: 10.1016/j.jmva.2017.03.006
• 发表时间: 2017-06
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Zheng Tingguo;Chen Rong
• 通讯作者: Chen Rong