I-Corps: Data-Enabled Forecasting Tools for Big Data

I-Corps：基于数据的大数据预测工具

• 批准号: 1338634
• 负责人: A Surjalal Sharma
• 金额: $ 5万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1338634&HistoricalAwards=false
• 关键词: Corps; Data; Enabled; Forecasting; Tools

This research explores the ability of data-derived modeling to extract, without a priori assumptions, the inherent features of a system from its time series data. Although the essential elements of this approach to modeling were developed for geospace phenomena, it is applicable to many natural as well as social phenomena. With the increasing importance of Big Data the fundamental nature of data-derived techniques provide a new approach to the modeling of systems from their time series data. Often such systems are not readily modeled using first principles and data-derived modeling can be the only viable approach. As in the case of geospace the dynamical models can lead to data-enabled forecasting tools. Another aspect of the research is the ability to quantify the variability of the system using a new fluctuation analysis, which yields improved fluctuation exponents such as the well-known Hurst index. The modeling of dynamical behavior, prediction, forecasting and characterization of is an integral part of the emerging data-enabled science.The data-enabled forecasting tools process large data sets to extract the essential features, predict the trends and quantify the forecasting ability. A broader impact of these tools is in addressing the need of many social and commercial systems for forecasts of future trends. In financial markets, the tool could yield forecasts for stock and other instruments, quantify the reliability of the forecasts, and predict changes in the short-term trends. In natural hazards, these techniques can be used to predict extreme events such as hurricanes, floods, earthquakes, and tsunamis from the time series data. The forecasting tools are independent of pre-determined models or parameters, and thus can provide reliable analyses of extreme events in commercial (financial markets, insurance) and social (disaster planning and management) sectors. A key need of Big Data is reliable analytic tools and the proposed data-enabled tools will address this need.

这项研究探讨了数据衍生的建模在没有先验假设的情况下提取系统的固有特征从其时间序列数据中提取的能力。尽管这种建模方法的基本要素是用于地理现象的，但它适用于许多自然和社会现象。随着大数据的越来越重要，数据衍生技术的基本性质为从其时间序列数据进行建模的系统提供了一种新的方法。通常，这种系统不容易使用第一原理建模，并且数据衍生的建模可能是唯一可行的方法。与地理空间一样，动态模型可以导致支持数据的预测工具。该研究的另一个方面是使用新的波动分析来量化系统变异性的能力，该分析得出了改善的波动指数，例如众所周知的Hurst指数。动态行为，预测，预测和表征的建模是新兴数据支持科学的组成部分。基于数据的预测工具处理大型数据集以提取基本功能，预测趋势并量化预测能力。这些工具的更广泛的影响是解决许多社会和商业系统以预测未来趋势的需求。在金融市场中，该工具可以对股票和其他工具进行预测，量化预测的可靠性，并预测短期趋势的变化。在自然灾害中，这些技术可用于预测时间序列数据中的飓风，洪水，地震和海啸等极端事件。预测工具独立于预定的模型或参数，因此可以对商业（金融市场，保险）和社会（灾难计划和管理）领域的极端事件进行可靠的分析。大数据的关键需求是可靠的分析工具，提出的支持数据的工具将满足这一需求。