Elements: Comprehensive Time Series Data?Analytics for the Prediction of Solar Flares and Eruptions

太阳耀斑是太阳系中最大的爆炸性事件之一。连同随之而来的爆发，太阳耀斑有可能扰乱我们所依赖的技术，如GPS、雷达、飞机与空中交通管制之间的高频无线电通信、依赖卫星的通信技术(如手机和互联网)以及电网配电网络。这个项目的目的是，首先，提高科学家对太阳活动区随时间变化的物理和统计行为的理解，以便我们能够利用他们的观测来预测它们是否以及何时会总体上发生耀斑或爆发，其次，使世界各地的科学家能够对太阳爆炸事件的预测进行可比较的、可重复的和数据驱动的研究。该项目及其先进的太阳耀斑预测软件基础设施将加强我国减轻太阳喷发潜在灾难性影响的努力。此外，通过帮助实现对太阳耀斑(已被视为自然灾害)的时间、位置和强度的可靠预测，该项目还将有助于减轻对其他类型基础设施的影响，如卫星(GPS、互联网、卫星通信)，这些基础设施不仅对我们的国防至关重要，而且对广泛的部门，如企业业务(如石油钻探)和通信也是至关重要的。该奖项还将通过佐治亚州立大学的教育和研究努力，投资于在跨学科环境中培训的受过高等教育、多样化、具有全球竞争力的STEM劳动力队伍的发展。该项目的目标是通过重新塑造最先进的数据挖掘技术来提高科学家对太阳喷发事件的理解。该项目将为对复杂的多变量时间序列数据集进行数据驱动的科学研究建立网络基础设施。这包括公开发布用于多变量时间序列分类、回归和聚类的数据挖掘研究的综合基准数据集，以及用于这些应用程序的开源软件(即公开发布预先训练的模型)。虽然该领域专注于太阳物理，但该软件和数据集可以使涉及事件跟踪和时空轨迹挖掘的其他领域受益(例如，监控移动、交通和天气数据分析以及商业预测的安全和医疗保健应用程序)。该项目还将在新的研究方向(最显著的是以前所未有的水平进行时间序列分析)的启发下，通过提供可重复使用的数据驱动的太阳耀斑预测，并在数据驱动、物理上可解释的机器学习模型的带头下，推动空间天气预报的研究。在这个方向上，这些项目将产生公开可用的耀斑预测大数据基准数据集，与国家科学和技术委员会的建议保持一致，并对数据科学界和空间气象界都有用。最后，该项目将通过上述在时间序列数据分析方面的前沿工作来推动数据挖掘和信息检索领域的计算机科学研究。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Solar flares are some of the largest explosive events in our solar system. Together with the accompanying eruptions, solar flares have the potential to disrupt the technology we rely on, such as GPS, radars, high-frequency radio communications between aircraft and air traffic control, communication technology that relies on satellites (such as cell phones and Internet), and electricity grid distribution networks. This project aims to, first, improve scientists' understanding of the time-dependent physical and statistical behavior of solar active regions to the point that we can exploit their observations to predict whether and when they will flare, or erupt, in general, and, second, to enable scientists worldwide to perform comparative, reproducible, and data-driven studies on the prediction of solar explosive events. This project, together with its advanced solar flare prediction software infrastructure, will strengthen our nation's efforts to mitigate the potentially catastrophic impact of solar eruptions. Moreover, by helping achieve reliable forecasts of the timing, location, and magnitude of solar flares (already considered as natural disasters), this project will also contribute to mitigating the impact on other types of infrastructure, such as satellites (GPS, Internet, satellite communications), that are critical to not only our national defense but also a broad range of sectors, such as enterprise operations (e.g., oil-drilling) and communications. This award will also invest in the development of a highly educated, diverse, globally competitive STEM workforce trained in an interdisciplinary environment through the educational and research efforts at Georgia State University. The goal of this project is to improve the scientists' understanding of eruptive solar events by re-shaping the state-of-the-art on data mining techniques. This project will build cyberinfrastructure for data-driven scientific research on complex multivariate time-series data sets. This includes public releases of comprehensive benchmark data sets ideal for data mining research on multivariate time series classification, regression, and clustering, as well as open-source software for these applications (i.e., the public release of pre-trained models). While the domain area is focused on solar physics, this software and data sets can benefit other domains that involve event tracking and mining of spatiotemporal trajectories (e.g., security and healthcare applications of monitoring movement, traffic and weather data analyses, and business predictions). The project will also advance research in Space Weather forecasting through the delivery of reproducible data-driven solar flare predictions, inspired by new research directions (most notably, time series analysis at an unprecedented level) and spearheaded by the data-driven, physically interpretable machine learning models. In this direction, the projects will produce publicly available flare forecasting big data benchmark data sets aligning with recommendations by the National Science and Technology Council and being useful to both Data Science and Space Weather communities. Finally, this project will advance Computer Science research in data mining and information retrieval areas through the above cutting-edge work on time series data analysis.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.