About the book
The objective of the book is to collect knowledge of the recent techniques and applications in time series analysis. The topics will cover basic theory from sampling theorem to Fourier and wavelet analysis and applications in signal and image analysis. The book should include diverse applications in signal and image analysis as medical signal analysis involving magnetic resonance imaging (MRI), positron emission tomography (PET) and neurophysiological neural spike trains, spatial and temporal data analysis, computer vision and financial modeling. It will also feature applications of machine learning to time series data such as independent component analysis (ICA), pattern recognition, supervised and unsupervised learning. The advance of computational tools or fast numerical methods will be highlighted in topics such as Fast Fourier Transform (FFT), spectral analysis in multivariate time series, principal component analysis (PCA), canonical correlation, singular value decomposition; discrete or continuous wavelet transforms, multiresolution analysis, especially based on open source software such as R, Python, and Julia.