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Synopsis
Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification.
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- PublisherLAP LAMBERT Academic Publishing
- Publication date2010
- ISBN 10 3838365879
- ISBN 13 9783838365879
- BindingPaperback
- LanguageEnglish
- Number of pages184
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High Performance Computing Applied to Nonlinear Time Series Analysis
Published by
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838365879
ISBN 13: 9783838365879
New
Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification. 184 pp. Englisch. Seller Inventory # 9783838365879
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High Performance Computing Applied to Nonlinear Time Series Analysis
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838365879
ISBN 13: 9783838365879
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Many applications of science and engineering, e.g. in physics, biology, economics or meteorology, are determined by dynamical systems. These systems evolve over time and then generate a set of data spaced in time, called time series. The analysis of time series from real systems, in terms of nonlinear dynamics, is the most direct link between chaos theory and the real world. Very useful information for making predictions about dynamical systems is extracted from the analysis of these time series. Since many of these applications must provide a real time response, it is necessary for analysis and prediction to be performed on a reasonable time scale. High Performance Computing gives a feasible solution to this problem, which enables it to be solved in an efficient manner. Nowadays, parallel computing is one of the most appropriate ways of obtaining important computational power. Thus, a set of high performance algorithms has been developed in this Thesis for both nonlinear time series analysis and, then, prediction. Finally, the Thesis proposes a method of time series modeling and predicting based on stochastic subspace system identification. Seller Inventory # 9783838365879
Quantity: 1 available
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High Performance Computing Applied to Nonlinear Time Series Analysis
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838365879
ISBN 13: 9783838365879
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High Performance Computing Applied to Nonlinear Time Series Analysis
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838365879
ISBN 13: 9783838365879
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