Primitives for Online Time Series Analysis

该项目旨在建立一个基本的基元库，用于在在线或接近在线的速度下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，这些传感器正在迅速部署在数量迅速增加的应用程序中，并且其数据速率继续急剧增加。快速(在线)响应在许多应用中是可取的(例如，望远镜对快速移动的感兴趣对象的远程对准)。时间序列数据量增长得越快，得出有关数据的汇总统计数据就越重要。该项目旨在为数千或更多时间序列集合上的基本统计数据的快速增量计算(如相关性和突发检测)提供工具。这项工作有两个推动力：开发用于计算这种统计的算法高效方法，以及对这些方法和已知的产生实际实施的方法进行工程。为了确保具体，研究和实施活动在一定程度上是由与物理学家和其他科学家的合作推动的。统计原语将在任何需要融合时间序列信息的应用中加强研究和国家网络基础设施。请注意，这项工作涵盖了各种时间序列，包括音乐和其他媒体的时间序列。这可能会吸引K-12的学生，通过哼唱查询等应用程序吸引他们学习科学。该项目的网站(http://cs.nyu.edu/cs/faculty/shasha/papers/statstream.html)被用来扩大成果的传播。

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.