时间序列中的高性能发现：技术和案例研究

English | PDF | 2004 | 195 Pages | ISBN : 0387008578 | 15.2 MB

Overview and Goals Data arriving in time order (a data stream) arises in fields ranging from physics to finance to medicine to music, just to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramati­ cally as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in orderto distill knowl­ edge from the data. On-line response is desirable in many applications (e.g., to aim a telescope at a burst of activity in a galaxy or to perform magnetic resonance-based real-time surgery). These factors - data size, bursts, correlation, and fast response­ motivate this book. Our goal is to help you design fast, scalable algorithms for the analysis of single or multiple time series. Not only will you find useful techniques and systems built from simple primi­ tives, but creative readers will find many other applications of these primitives and may see how to create new ones of their own. Our goal, then, is to help research mathematicians and computer scientists find new algorithms and to help working scientists and financial mathematicians design better, faster software.

中文| PDF | 2004 | 195页| ISBN:0387008578| 15.2 MB概述和目标按时间顺序到达的数据（数据流）出现在从物理学到金融、医学到音乐的各个领域，仅举几例。数据通常来自传感器（例如物理学和医学），随着传感器技术的改进，传感器的数据速率继续大幅提高。此外，传感器的数量正在增加，因此传感器之间的数据关联变得越来越重要，以便从数据中提取知识。在线响应在许多应用中都是可取的（例如，将望远镜对准星系中的突发活动或进行基于磁共振的实时手术）。这些因素——数据大小、突发、相关性和快速响应——激励着这本书。我们的目标是帮助您设计快速、可扩展的算法，用于分析单个或多个时间序列。你不仅会发现用简单的原语构建的有用技术和系统，而且有创造力的读者会发现这些原语的许多其他应用，并可能看到如何创建自己的新原语。因此，我们的目标是帮助研究数学家和计算机科学家找到新的算法，并帮助在职科学家和金融数学家设计更好、更快的软件。

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