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

本研究探讨的能力，数据衍生的建模提取，没有先验假设，系统的内在特征，从其时间序列数据。虽然这种建模方法的基本要素是为地球空间现象开发的，但它适用于许多自然和社会现象。随着大数据的重要性日益增加，数据衍生技术的基本性质为从时间序列数据建模系统提供了一种新的方法。通常，这样的系统不容易使用第一原理建模，数据导出的建模可能是唯一可行的方法。与地球空间的情况一样，动态模型可以导致数据支持的预测工具。研究的另一个方面是使用新的波动分析来量化系统的可变性的能力，该分析产生改进的波动指数，例如众所周知的赫斯特指数。动态行为的建模、预测、预测和表征是新兴的数据使能科学的一个组成部分。数据使能预测工具处理大数据集以提取基本特征，预测趋势并量化预测能力。这些工具的一个更广泛的影响是解决了许多社会和商业系统对未来趋势预测的需求。在金融市场，该工具可以预测股票和其他工具，量化预测的可靠性，并预测短期趋势的变化。在自然灾害中，这些技术可以用来预测极端事件，如飓风，洪水，地震和海啸的时间序列数据。预测工具不依赖于预先确定的模型或参数，因此可以对商业（金融市场、保险）和社会（灾害规划和管理）部门的极端事件提供可靠的分析。大数据的一个关键需求是可靠的分析工具，而拟议的数据支持工具将满足这一需求。

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.