NDLI: Fast algorithms for time series with applications to finance, physics, music, biology, and other suspects

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The next database revolution

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Fast algorithms for time series with applications to finance, physics, music, biology, and other suspects

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Fast algorithms for time series with applications to finance, physics, music, biology, and other suspects

Content Provider	ACM Digital Library
Author	Shasha, Dennis Lerner, Alberto Zhao, Xiaojian Zhu, Yunyue Wang, Zhihua
Abstract	Financial time series streams are watched closely by millions of traders. What exactly do they look for and how can we help them do it faster? Physicists study the time series emerging from their sensors. The same question holds for them. Musicians produce time series. Consumers may want to compare them. This tutorial presents techniques and case studies for four problems:1. Finding sliding window correlations in financial, physical, and other applications.2. Discovering bursts in large sensor data of gamma rays.3. Matching hums to recorded music, even when people don't hum well.4. Maintaining and manipulating time-ordered data in a database setting.This tutorial draws mostly from the book High Performance Discovery in Time Series: techniques and case studies, Springer-Verlag 2004. You can find the power point slides for this tutorial at http://cs.nyu.edu/cs/faculty/shasha/papers/sigmod04.ppt.The tutorial is aimed at researchers in streams, data mining, and scientific computing. Its applications should interest anyone who works with scientists or financial "quants." The emphasis will be on recent results and open problems. This is a ripe area for further advance.
Starting Page	965
Ending Page	968
Page Count	4
File Format	PDF
ISBN	1581138598
DOI	10.1145/1007568.1007726
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-06-13
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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