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

  • Authors:

  • Alberto Lerner;Dennis Shasha;Zhihua Wang;Xiaojian Zhao;Yunyue Zhu

  • Affiliations:

  • New York University, New York, NY;New York University, New York, NY;New York University, New York, NY;New York University, New York, NY;New York University, New York, NY

  • Venue:
  • SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
  • Year:
  • 2004

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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.