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Applied Time Series Analysis and Innovative Computing

Author: Sio-Iong Ao
Publisher: Dordrecht : Springer Science+Business Media B.V., 2010.
Series: SpringerLink: Springer e-Books; Lecture Notes in Electrical Engineering, 59.
Edition/Format:   Computer file : English : Online-ausgView all editions and formats
Summary:

This text is a systematic, state-of-the-art introduction to the use of innovative computing paradigms as an investigative tool for applications in time series analysis. It includes frontier case  Read more...

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Material Type: Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Sio-Iong Ao
ISBN: 9789048187683 9048187680 9789048187676 9048187672
OCLC Number: 664676988
Reproduction Notes: Online-Ausg. Berlin [u.a.] Springer, 2010. online-Ressource.
Description: S.
Contents: Chapter 1. Introduction1 Applied Time Series Analysis 1.2 Advances in Innovative Computing Paradigms 1.3 Real-World Applications: Innovative Computing Paradigms for Time Series ProblemsChapter 2. Applied Time Series Analysis2.1 Basic Characteristics of Time Series2.2 Autoregression and ARIMA Models2.3 Mathematical Models in the Frequency DomainChapter 3. Advances in Innovative Computing Paradigms 3.1 Research Advances in Computing Algorithms and Databases 3.2 Research Advances in Integration of Hardware, Systems and Networks3.3 Research Advances in Internet, Web and Grid Computing 3.4 Research Advances in Visualization, Design and Communication3.5 Advances and Applications for Time Series Problems3.6 An Illustrative Example of Building an Innovative Computing Algorithm for Simulated Time SeriesChapter 4. Real-Word Application I: Developing Innovative Computing Algorithms for Business Time Series 4.1 Business Time Series 4.2 Advances in Business Forecasting 4.3 Developing a Hybrid Intelligent Econometrics Model for Business Forecasting 4.4 Application for Tourism Demand Forecasting4.5 Application for Cross-Market Financial Forecasting4.6 Discussions and Further WorksChapter 5. Real-Word Application II: Developing Innovative Computing Algorithms for Biological Time Series 5.1 Biological Time Series 5.2 Advances in Experimental Designs for Microarray Time Series 5.3 Reverse Engineering of Biological Networks 5.4 Models for Biological Network Inference5.5 Discussions and Further Works Chapter 6. Real-Word Application III: Developing Innovative Computing Algorithms for Astronomical Time Series6.1 Astronomical Time Series 6.2 Advances and Applications of Innovative Computing Paradigms6.3 Motivations for Investigating the Quasar Time Series with Innovative Approaches6.4 Advances in Emerging Methods for Quasar Studies 6.5 Autocorrelations Analysis of Quasars and Clustering Approach6.6 Discussions and Further Works
Series Title: SpringerLink: Springer e-Books; Lecture Notes in Electrical Engineering, 59.
Responsibility: by Sio-Iong Ao.

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