| Vorliegende Sprache |
eng |
| ISBN |
978-90-481-8767-6 |
| Name |
Ao, Sio-Iong |
| T I T E L |
Applied Time Series Analysis and Innovative Computing |
| Verlagsort |
Dordrecht |
| Verlag |
Springer Science+Business Media B.V |
| Erscheinungsjahr |
2010 |
| 2010 |
| Umfang |
Online-Ressource (XIV, 112p, online resource) |
| Reihe |
Lecture Notes in Electrical Engineering ; 59 |
| Notiz / Fußnoten |
Description based upon print version of record |
| Enthaltene Werke |
$t; Applied Time Series Analysis and Innovative Computing; Chapter 1; Introduction; 1.1 Applied Time Series Analysis; 1.1.1 Basic Definitions; 1.1.2 Basic Applied Time Series Models; 1.1.3 Frequency Domain Models; 1.2 Advances in Innovative Computing Paradigms; 1.2.1 Computing Algorithms and Databases; 1.2.2 Integration of Hardware, Systems and Networks; 1.2.3 Internet, Web and Grid Computing; 1.2.4 Visualization, Design and Communication; 1.3 Real-World Applications: Innovative Computing Paradigms for Time Series Problems. 1.3.1 Developing Innovative Computing Algorithms for Business Time Series1.3.2 Developing Innovative Computing Algorithms for Biological Time Series; 1.3.3 Developing Innovative Computing Algorithms for Astronomical Time Series; Chapter 2; Applied Time Series Analysis; 2.1 Basic Characteristics of Time Series; 2.1.1 Estimation of Correlation; 2.1.1.1 Auto-Correlation Analysis; 2.1.1.2 Cross-Correlation Analysis; 2.1.1.3 Autocorrelation Functions; 2.1.2 Stationary Time Series; 2.1.3 Smoothing of the Time Series; 2.1.4 Periodogram Analysis; 2.2 Autoregression and ARIMA Models. 2.2.1 Time Series Regression2.2.2 Autoregressive Moving Average Models; 2.2.3 Building ARIMA Models; 2.2.4 Forecasting and Evaluation; 2.2.5 Causality of the Time Series; 2.3 Mathematical Models in the Frequency Domain; 2.3.1 Introduction; 2.3.2 Discrimination Analysis; 2.3.3 Clustering Analysis; 2.3.4 Principal Components and Factor Analysis; 2.3.5 Dynamic Fourier Analysis; 2.3.6 Random Coefficient Regression; 2.3.7 Discrete Fourier Transform; Chapter 3; Advances in Innovative Computing Paradigms; 3.1 Research Advances in Computing Algorithms and Databases; 3.1.1 Knowledge Extraction Methods. 3.1.2 Exploiting Large Complex Databases3.1.3 Neural Computing Algorithms; 3.1.4 Fuzzy Computing Algorithms; 3.1.5 Evolutionary Computing Algorithms; 3.1.6 Quantum Computing Algorithms; 3.1.7 Swarm-Based Computing Algorithms; 3.1.8 DNA Computing Algorithms; 3.1.9 Theoretical Modeling and Simulations; 3.2 Research Advances in Integration of Hardware, Systems and Networks; 3.2.1 Innovative Experimental Hardware System; 3.2.2 Data-Acquisition Devices; 3.2.3 Interaction Devices for Visual Exploration; 3.2.4 Graphics Processing Units and Co-Processors for Innovative Computing. 3.2.5 Networking and Interoperability3.2.6 Code Optimization and Integration; 3.3 Research Advances in Internet, Web and Grid Computing; 3.3.1 Distributed Computation and Data Sharing; 3.3.2 Large-Scale Collaborations over the Internet; 3.3.3 Grid Computing; 3.3.4 Pooling of Remote Computer Resources; 3.3.5 Integration of Knowledge Metadata Systems; 3.4 Research Advances in Visualization, Design and Communication; 3.4.1 Novel Solutions to Visualization and Communication Challenges; 3.4.2 Displaying of Complex Information; 3.4.3 Escaping Flatland. 3.4.4 Systems Integration for High Performance Image Processing |
| Titelhinweis |
Erscheint auch als (Druck-Ausgabe)ISBN: 978-904-818-767-6 |
| ISBN |
ISBN 978-90-481-8768-3 |
| Klassifikation |
TBJ |
| MAT003000 |
| PBK |
| MAT034000 |
| *68-02 |
| 68U01 |
| 68M10 |
| 519 |
| 515 |
| 519.550285 |
| TA329-348 |
| TA640-643 |
| Kurzbeschreibung |
Applied Time Series Analysis -- Advances in Innovative Computing Paradigms -- Real-Word Application I: Developing Innovative Computing Algorithms for Business Time Series -- Real-Word Application II: Developing Innovative Computing Algorithms for Biological Time Series -- Real-Word Application III: Developing Innovative Computing Algorithms for Astronomical Time Series. |
| 2. Kurzbeschreibung |
Applied Time Series Analysis and Innovative Computing contains the applied time series analysis and innovative computing paradigms, with frontier application studies for the time series problems based on the recent works at the Oxford University Computing Laboratory, University of Oxford, the University of Hong Kong, and the Chinese University of Hong Kong. The monograph was drafted when the author was a post-doctoral fellow in Harvard School of Engineering and Applied Sciences, Harvard University. It provides a systematic introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. Topics covered include Frequency Domain, Correlation, Smoothing, Periodogram, Autoregression, ARIMA Models, Discrimination Analysis, Clustering Analysis, Factor Analysis, Dynamic Fourier Analysis, Random Coefficient Regression, Discrete Fourier Transform, Innovative Computing Algorithms, Knowledge Extraction, Large Complex Databases, Modeling and Simulations, Integration of Hardware, Systems and Networks, Grid Computing, Visualization, Design and Communication, Business Time Series Applications, Biological Time Series Applications, and Astronomical Time Series Applications. Applied Time Series Analysis and Innovative Computing offers the state of art of tremendous advances in applied time series analysis and innovative computing paradigms and also serves as an excellent reference work for researchers and graduate students working on applied time series analysis and innovative computing paradigms. |
| SWB-Titel-Idn |
323618952 |
| Signatur |
Springer E-Book |
| Bemerkungen |
Elektronischer Volltext - Campuslizenz |
| Elektronische Adresse |
$uhttp://dx.doi.org/10.1007/978-90-481-8768-3 |
| Internetseite / Link |
Volltext |
| Siehe auch |
Volltext |
| Siehe auch |
Cover |
| Siehe auch |
Inhaltsverzeichnis |
| Siehe auch |
Einführung/Vorwort |
| Siehe auch |
Inhaltstext |
