CRII: OAC: Cyberinfrastructure for Machine Learning on Multivariate Time Series Data and Functional Networks

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。从天气分析到脑区活动分析，从交通流分析到金融趋势分析，多变量时间序列（MVTS）数据已广泛用于许多领域的预测和探索性任务。MVTS实例使用相互依赖变量的多个时间序列来表示动态系统和自然事件的状态。功能网络通过寻找MVTS变量之间的高阶关系来利用它们之间的相互作用。数据表示（MVTS或功能网络）的适当选择对机器学习（ML）工作提出了挑战，可能会影响下游任务（如分类、回归和聚类）的性能。该项目将开发公共的、基于网络的、支持图形用户界面（GUI）的网络基础设施，并在两种数据表示上实现新颖的和以前开发的预测、探索和生成任务。该项目通过促进基于mvts的太阳磁场数据的太阳耀斑预测来促进太阳物理科学的进步，并通过改进基于功能磁共振成像的脑功能网络的神经系统疾病（如精神分裂症）预测模型来促进国民健康，从而为国家利益服务。研究成果，包括开发的网络基础设施和设计的机器学习模型，将为涉及本科生（包括来自代表性不足群体的学生）的跨学科研究、课程课程开发和高中外展活动提供机会。MVTS实例由多个传感器的时间序列记录组成。在函数网络中，节点代表变量，边代表对应节点时间序列的统计相似度。虽然在MVTS表示中保留了数据的完整性，但由于传感器故障等事件导致的时间序列中的噪声或缺失数据可能会损害下游ML任务的性能。功能网络表示有助于利用变量的多跳关系，但网络构建中依赖阈值的稀疏性可能使ML模型失去重要特征。MVTS和功能网络数据集的机器学习挑战包括数据表示的适当选择和有限数量的训练样本（特别是在医学领域）。该项目将提供一个统一的框架，通过应用和定制当代ML模式（如矩阵/张量分解、序列模型、图神经网络（GNN）和动态图嵌入），在数据表示上执行（非）监督ML任务。该项目还将提供一个框架，通过自回归、基于自编码器和对抗模型，用合成训练样本增加数据集。该项目将设计并实施一个基于网络的系统，该系统包含数据导入和准备、表示学习、数据增强、验证、结果可视化以及导出衍生和合成数据集的模块。web平台将托管在公共领域，其基于gui的前端将使研究人员能够应用后端机器学习模型，而无需使用机器学习库进行显式编程。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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).From weather analysis to brain region activity analysis, from traffic flow analysis to financial trend analysis, multivariate time series (MVTS) data have been used extensively in predictive and exploratory tasks for numerous domains. MVTS instances represent states of dynamical systems and natural events using multiple time series of interdependent variables. Functional networks leverage the interactions of MVTS variables by finding higher-order relationships among them. The appropriate choice of data representation (MVTS or functional network) poses a challenge in machine learning (ML) efforts that can affect the performance of downstream tasks such as classification, regression, and clustering. This project will develop cyberinfrastructure that is public, web-based, and Graphical User Interface (GUI)-enabled and enables both novel and previously developed predictive, exploratory, and generative tasks on both data representations. The project serves the national interest by promoting the progress of solar physics science through facilitating solar flare prediction from MVTS-based solar magnetic field data and advancing national health through improving prediction models for neurological diseases (e.g., Schizophrenia) from fMRI-based functional brain networks. The research outcomes, including the cyberinfrastructure developed and ML models designed, will provide an opportunity for interdisciplinary research involving undergraduate students including those from underrepresented groups, for course curriculum development, and for high school outreach activities. MVTS instances are formed from the time series records of multiple sensors. In functional networks, the nodes represent the variables, and the edges represent the statistical similarity of the time series of the corresponding nodes. While in the MVTS representation completeness of data is preserved, noisy or missing data in time series due to events such as faults in sensors can compromise the performance of downstream ML tasks. Functional network representations help leverage multi-hop relationships of the variables, but the threshold-dependent sparsity in network construction can make ML models lose important features. Machine learning challenges of MVTS and functional network datasets include the appropriate choice of data representation and the limited number of training samples (especially in the medical domain). This project will provide a unified framework for performing (un)-supervised ML tasks on both data representations through application and customization of contemporary ML modes such as matrix/tensor decomposition, sequence models, Graph Neural Networks (GNN), and dynamic graph embedding. The project will also provide a framework for augmenting datasets with synthetic training samples through autoregressive, autoencoder-based, and adversarial models. The project will design and implement a web-based system that contains modules for data import and preparation, representation learning, data augmentation, validation, result visualization, and for exporting derived and synthetic datasets. The web platform will be hosted in the public domain, and its GUI-based front end will enable researchers to apply back-end ML models without explicitly programming using ML libraries.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.