Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
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Over recent years, a new class of methods has been developed, belonging to the blind source separation (BSS) area of research, whose promise is to reveal the driving forces which underlie an observed data sequence. Actually, each measured signal depends on several distinct underlying factors, being essentially a mixture of them. It is these factors or source signals that are of primary interest, but they are buried within a large set of measured signal mixtures. Separating a relatively small number of distinct time-varying causal factors becomes crucial in multivariate financial time series analysis, when attempting to explain past comovements and to predict future evolutions. In this paper, BSS techniques are addressed for revealing fundamental factors behind 16 parallel series of foreign exchange rates. More accurate predictions can be performed via these independent components, after their separation, using different forecasting strategies and settings. Denoising and change-point detection can be also carried out for each independent component in turn.