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. 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.

InhaltsangabeChapter 1. Introduction 1 Applied Time Series Analysis 1.2 Advances in Innovative Computing Paradigms 1.3 RealWorld Applications: Innovative Computing Paradigms for Time Series Problems Chapter 2. Applied Time Series Analysis 2.1 Basic Characteristics of Time Series 2.2 Autoregression and ARIMA Models 2.3 Mathematical Models in the Frequency Domain Chapter 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 Networks 3.3 Research Advances in Internet, Web and Grid Computing 3.4 Research Advances in Visualization, Design and Communication 3.5 Advances and Applications for Time Series Problems 3.6 An Illustrative Example of Building an Innovative Computing Algorithm for Simulated Time Series Chapter 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 Forecasting 4.5 Application for Cross-Market Financial Forecasting 4.6 Discussions and Further Works Chapter 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 Inference 5.5 Discussions and Further Works Chapter 6. Real-Word Application III: Developing Innovative Computing Algorithms for Astronomical Time Series 6.1 Astronomical Time Series 6.2 Advances and Applications of Innovative Computing Paradigms 6.3 Motivations for Investigating the Quasar Time Series with Innovative Approaches 6.4 Advances in Emerging Methods for Quasar Studies 6.5 Autocorrelations Analysis of Quasars and Clustering Approach 6.6 Discussions and Further Works