Items related to Applied Time Series Analysis and Innovative Computing
Synopsis
Chapter 1. Introduction 1 Applied Time Series Analysis 1.2 Advances in Innovative Computing Paradigms 1.3 Real-World 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
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