[PDF.27jl] High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)
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High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)
New York University
[PDF.hw74] High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)
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| #3906823 in Books | 2004-06-03 | Original language:English | PDF # 1 | 9.21 x.50 x6.14l,.92 | File type: PDF | 190 pages||1 of 1 people found the following review helpful.| Solid but overpriced|By Dimitri Shvorob|Table of contents, accessible through "Search inside this book", shows Part I dominated by discussion of data-compression methods, including DFT, DWT, SVD and (barely) random projection, followed by 12-page chapters on indexing and similarity search, with the latter centered on dynamic time warping. Part II presents three applications, wi|From the Back Cover|| |Time-series data―data arriving in time order, or a data stream―can be found in fields such as physics, finance, music, networking, and medical instrumentation. Designing fast, scalable algorithms for analyzing single or multiple
This monograph is a technical survey of concepts and techniques for describing and analyzing large-scale time-series data streams. Some topics covered are algorithms for query by humming, gamma-ray burst detection, pairs trading, and density detection. Included are self-contained descriptions of wavelets, fast Fourier transforms, and sketches as they apply to time-series analysis. Detailed applications are built on a solid scientific basis.
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