Primitives for Online Time Series Analysis

在线时间序列分析的基元

• 批准号: 0414763
• 负责人: Dennis Shasha
• 金额: $ 36万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2009-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414763&HistoricalAwards=false
• 关键词: Primitives; Online; Time; Series; Analysis

This project aims to build a foundational library of primitives for calculating statistics on time series at online or near-online speeds. A time series is simply a collection of data arriving in time order. They arise in fields ranging from physics to finance or medicine or music. Often the data comes from sensors, that are becoming deployed in rapidly increasing number of applications and whose data rates are continuing to increase sharply. Fast (online) response is desirable in many applications (e.g., remote aiming of a telescope to a fast-moving object of interest). The faster the volumes of the time series data grow, the more important it is to derive summary statistics about the data. This project aims to provide tools for the rapid incremental computation of basic statistics (such as correlation and burst detection) on collections of thousands or more time series. This work has two thrusts: the development of algorithmically efficient methods for calculating such statistics and the engineering of these methods and already known approaches to yield practical implementations. To ensure concreteness, the research and implementation activities are driven in part by collaborations with physicists and other scientists. The statistical primitives will enhance research and the national cyberinfrastructure in any application requiring the fusion of time series information. Note that this work encompasses all kinds of time series, including time series for music and other media. This may attract K-12 students, drawing them to science through applications such as query by humming. The project's Web site (http://cs.nyu.edu/cs/faculty/shasha/papers/statstream.html) is used to broaden dissemination of results.

该项目旨在建立一个基本的原语库，用于以在线或接近在线的速度计算时间序列的统计数据。时间序列只是按时间顺序到达的数据的集合。它们出现在从物理到金融、医学或音乐的各个领域。数据通常来自传感器，这些传感器正在部署在数量迅速增加的应用程序中，并且其数据速率持续急剧增加。快速（在线）响应在许多应用（例如，将望远镜远程瞄准快速移动的感兴趣物体）。时间序列数据量增长得越快，获取有关数据的汇总统计信息就越重要。该项目旨在提供工具，用于对数千个或更多时间序列的集合进行基本统计（如相关性和突发检测）的快速增量计算。这项工作有两个重点：用于计算这种统计的算法有效方法的开发以及这些方法和已知方法的工程化以产生实际实现。为了确保具体性，研究和实施活动部分是由与物理学家和其他科学家的合作驱动的。统计原语将在需要融合时间序列信息的任何应用中加强研究和国家网络基础设施。请注意，这项工作涵盖了所有类型的时间序列，包括音乐和其他媒体的时间序列。这可能会吸引K-12学生，通过哼唱查询等应用程序将他们吸引到科学领域。该项目的网站（http：//cs.nyu.edu/cs/faculty/shasha/papers/statstream.html）用于扩大成果的传播。