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.