[PDF.52lc] High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)

High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)

[PDF.qp84] High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)

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 | #3434562 in Books |  2004-06-03 | Original language:English | PDF # 1 |  9.21 x.50 x6.14l,.92 | File type: PDF | 190 pages

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||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.

You easily download any file type for your gadget.High Performance Discovery In Time Series: Techniques and Case Studies (Monographs in Computer Science)   |  New York University. Which are the reasons I like to read books. Great story by a great author.

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