High Performance Discovery In Time Series: Techniques And Case Studies (Monographs In Computer Science) - Couverture souple

New York University, .

9781441918420: High Performance Discovery In Time Series: Techniques And Case Studies (Monographs In Computer Science)

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ISBN 10 : 1441918426 ISBN 13 : 9781441918420

Editeur : Springer, 2011

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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.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

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�diteur: Springer
Date d'�dition: 2011
Langue: anglais
ISBN 10: 1441918426
ISBN 13: 9781441918420
Reliure: Broch�
Nombre de pages: 208
Coordonn�es du fabricant: GPSR Kontakt
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New York University:High Performance Di (eng)

New York University Courant Inst. Mathematical Sciences

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ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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High Performance Discovery In Time Series

Donna Ryan

Edit� par Springer New York Dez 2011, 2011

ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 208 pp. Englisch. N� de r�f. du vendeur 9781441918420

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

New York University, .

Edit� par Springer, 2011

ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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High Performance Discovery In Time Series

New York University

Edit� par Springer New York, 2011

ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 worldThis monograph is a te. N� de r�f. du vendeur 4172416

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High Performance Discovery In Time Series

New York University, .

Edit� par Springer, 2011

ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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Etat : New. pp. 208. N� de r�f. du vendeur 263594836

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High Performance Discovery In Time Series : Techniques and Case Studies

Donna Ryan

Edit� par Springer, 2011

ISBN 10 : 1441918426 ISBN 13 : 9781441918420

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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Overview and Goals Data arriving in time order (a data stream) arises in fields ranging from physics to finance to medicine to music, just to name a few. Often the data comes from sensors (in physics and medicine for example) whose data rates continue to improve dramati cally as sensor technology improves. Further, the number of sensors is increasing, so correlating data between sensors becomes ever more critical in orderto distill knowl edge from the data. On-line response is desirable in many applications (e.g., to aim a telescope at a burst of activity in a galaxy or to perform magnetic resonance-based real-time surgery). These factors - data size, bursts, correlation, and fast response motivate this book. Our goal is to help you design fast, scalable algorithms for the analysis of single or multiple time series. Not only will you find useful techniques and systems built from simple primi tives, but creative readers will find many other applications of these primitives and may see how to create new ones of their own. Our goal, then, is to help research mathematicians and computer scientists find new algorithms and to help working scientists and financial mathematicians design better, faster software. N� de r�f. du vendeur 9781441918420

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