Overview
- Describes how to discover groups of time series highly correlated with one another and how to make existing series fast and efficient for such purposes as scientific discovery, medical diagnosis, and profit in the business world
Part of the book series: Monographs in Computer Science (MCS)
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Table of contents(8 chapters)
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Review of Techniques
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Case Studies
About this book
Reviews
From the reviews:
"The goal of the book is to show how to design fast scalable algorithms for the analysis of time series when much data must be analyzed. … A linear time filter is constructed in such a way that no burst will be missed and nearly all false positives are eliminated. … the book aims at efficient discovery in time series and presents practical algorithms for this task." (Jiri Andel, Mathematical Reviews, 2005)
Authors and Affiliations
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Courant Institute, New York, USA
Dennis Shasha, Yunyue Zhu
Bibliographic Information
Book Title: High Performance Discovery In Time Series
Book Subtitle: Techniques and Case Studies
Authors: Dennis Shasha, Yunyue Zhu
Series Title: Monographs in Computer Science
DOI: https://doi.org/10.1007/978-1-4757-4046-2
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Dennis E. Shasha and Yunyue Zhu 2004
Hardcover ISBN: 978-0-387-00857-8Published: 03 June 2004
Softcover ISBN: 978-1-4419-1842-0Published: 12 December 2011
eBook ISBN: 978-1-4757-4046-2Published: 09 November 2013
Series ISSN: 0172-603X
Series E-ISSN: 2512-5486
Edition Number: 1
Number of Pages: XV, 190
Number of Illustrations: 45 b/w illustrations
Topics: Models and Principles, Information Storage and Retrieval, Algorithm Analysis and Problem Complexity, Artificial Intelligence, Performance and Reliability, Math Applications in Computer Science