CDS&E: Statistical Methodology for Analysis and Forecasting with Large Scale Temporal Data

CDS

基本信息

批准号：
1821220
负责人：
George Michailidis
金额：
$ 20万
依托单位：
University of Florida
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-01 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1821220&HistoricalAwards=false
关键词：
CDS&amp Statistical Methodology Analysis Forecasting

项目摘要

Technological advances have enabled the collection of large, complex data that evolve over time. Such data also exhibit heterogeneity across multiple entities (e.g. countries, patients) and on many occasions are sampled collected) at different frequencies. Hence, there is a strong need for developing and tailoring data analysis techniques to the specific requirements imposed by the presence of temporal dependence across multiple variables and also address varying sampling frequency and heterogeneity issues. The statistical learning models, and associated analysis methods developed in this project would be applicable across a wide range of fields, including analysis and forecasting with macroeconomic and financial data and in neuroscience. Empirical work based on the work of this project would provide insights on functional connectivity of brain regions, but also quantify the degree of heterogeneity of subjects suffering from a common disease. They would also be useful to policy makers and financial regulators for devising monitoring schemes that assess stress conditions across markets. Further, we expect significant technology transfer to other application areas, such as environmental sciences where similar types of data, characterized by heterogeneity and mixed frequency sampling, are available. To address the challenges of temporal dependence, heterogeneity and varying sampling frequency in the data this project would: (i) develop and investigate Bayesian versions of Vector Autoregressive (VAR) models for high-dimensional time series data, based on novel prior distributions, (ii) introduce structured sparsity in VAR models and also incorporate exogenous variables, (iii) develop approximate dynamic factor models that can accommodate strongly correlated idiosyncratic components, (iv) develop methods for joint estimation of related VAR models and finally (v) develop Bayesian methodology for handling mixed frequency time series data. A strong emphasis is placed on providing uncertainty quantification of the model parameters, which is particularly important in applications and is usually lacking in many modern methods for large data sets. This project would advance the state of the art for Big Data settings involving a large number of time series both at the modeling, computational and inference fronts. Finally, doctoral students would receive mentoring in novel, timely topics on time series modeling, analysis and forecasting and course curriculum would be advanced.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.

技术进步使得随着时间的流逝而发展的大型，复杂的数据。这些数据还以不同的频率在多个实体（例如国家，患者）和许多情况下进行采样的多个实体（例如，患者）表现出异质性。因此，非常需要开发和调整数据分析技术，以跨多个变量存在时间依赖性所施加的特定要求，并解决不同的采样频率和异质性问题。该项目中开发的统计学习模型以及相关的分析方法将适用于广泛的领域，包括分析和预测宏观经济和财务数据以及神经科学。基于该项目的工作的经验工作将提供有关大脑区域功能连通性的见解，但也量化了患有常见疾病的受试者的异质性程度。它们也对制定者和财务监管机构也很有用，可以设计监视各个市场压力条件的计划。此外，我们预计将大量的技术转移到其他应用领域，例如环境科学，在环境科学中，具有异质性和混合频率采样的类型类型的数据类型。 To address the challenges of temporal dependence, heterogeneity and varying sampling frequency in the data this project would: (i) develop and investigate Bayesian versions of Vector Autoregressive (VAR) models for high-dimensional time series data, based on novel prior distributions, (ii) introduce structured sparsity in VAR models and also incorporate exogenous variables, (iii) develop approximate dynamic factor models that can accommodate strongly correlated特质组件，（iv）开发了相关VAR模型联合估计的方法，最后（V）开发了用于处理混合频率时间序列数据的贝叶斯方法。非常重点是提供模型参数的不确定性量化，这在应用程序中尤为重要，并且通常在许多现代方法中缺乏大型数据集。该项目将推动大数据设置的最新技术，涉及建模，计算和推理方面的大量时间序列。最后，博士生将在时间序列建模，分析和预测和课程课程中获得小说，及时的主题的指导。该奖项反映了NSF的法定任务，并认为通过基金会的知识分子的智力优点和更广泛的影响，认为值得通过评估来获得支持。