CAREER: Structure Learning and Forecasting of Large-Scale Time Series

职业：大规模时间序列的结构学习和预测

• 批准号: 2239102
• 负责人: Sumanta Basu
• 金额: $ 45万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239102&HistoricalAwards=false
• 关键词: CAREER; Structure; Learning; Forecasting; Large

In many areas of modern biological and social sciences, researchers and practitioners seek to gain insight into the dynamics of a complex system using large-scale time series data sets. Examples include gene regulatory network reconstruction using time-course gene expression data sets, functional connectivity analysis of brain network architecture using neurophysiological signals, and monitoring systemic risk in the financial market using historical data on many firms' stock prices. The overarching goal of this project is to develop scalable statistical methods for learning such dynamic relationships using high-dimensional time series (HDTS) data sets, and provide a rigorous analysis of their properties. These methods, upon successful completion, are expected to aid data-driven testable hypothesis generation in systems biology, imaging-based biomarker search in computational neuroscience, and inform regulatory policy for financial risk management and monitoring.The research outcomes will be integrated into a number of education and outreach activities, including development of a modern data science curriculum with an accompanying online textbook as well as training of graduate and undergraduate students.Existing algorithms for analyzing HDTS data sets rely primarily on using modern regularization in machine learning coupled with a squared error loss designed for independent data. This is in sharp contrast with the core modeling philosophy of classical time series, where temporal dependence among observations is explicitly encoded in the likelihood or loss function to increase the accuracy of structure learning and prediction. This project will narrow the gap by designing new algorithms where temporal dependence and regularization inform each other using dependence-aware machine learning methods. In particular, impulse response and quantile-specific graphical models in the time domain, adaptively regularized graphical models in the frequency domain, and random forests that explicitly incorporate temporal dependence in building regression trees, will be developed. These methods will be validated on real data sets from genomics, neuroscience and financial economics in consultation with domain experts. Results will be disseminated to public by publishing peer-reviewed articles in statistics, machine learning and other scientific journals. Software implementations of algorithms developed in this project will be made publicly available in the form of R packages.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.

在现代生物和社会科学的许多领域，研究人员和实践者试图通过大规模时间序列数据集深入了解复杂系统的动态。例子包括使用时间过程基因表达数据集进行基因调控网络重建，使用神经生理信号对大脑网络结构进行功能连接分析，以及使用许多公司股票价格的历史数据监测金融市场中的系统性风险。该项目的总体目标是开发可扩展的统计方法，用于使用高维时间序列（HDTS）数据集学习这种动态关系，并提供对其特性的严格分析。这些方法一旦成功完成，预计将有助于系统生物学中数据驱动的可测试假设生成，计算神经科学中基于成像的生物标志物搜索，并为金融风险管理和监测的监管政策提供信息。研究成果将被整合到一些教育和推广活动中，包括开发一个现代数据科学课程和配套的在线教科书，以及对研究生和本科生的培训。现有的分析HDTS数据集的算法主要依赖于使用机器学习中的现代正则化，以及为独立数据设计的平方误差损失。这与经典时间序列的核心建模理念形成鲜明对比，在经典时间序列中，观测值之间的时间依赖性被明确地编码在似然函数或损失函数中，以提高结构学习和预测的准确性。该项目将通过设计新的算法来缩小差距，其中时间依赖性和正则化使用依赖感知的机器学习方法相互通知。特别是，将开发时域的脉冲响应和分位数特定图形模型，频域的自适应正则化图形模型，以及在构建回归树时明确包含时间依赖性的随机森林。这些方法将与领域专家协商，在基因组学、神经科学和金融经济学的真实数据集上进行验证。研究结果将通过在统计学、机器学习和其他科学期刊上发表同行评议文章的方式向公众传播。在这个项目中开发的算法的软件实现将以R包的形式公开提供。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。