Wavelet-Based Entropy Methods in the Analysis of Chaotic and Complex Systems

Social systems, characterized by human interaction, inherently exhibit disorder, unpredictability, and complexity traits. The application of entropy measures has become crucial in the data analysis of these chaotic and complex systems. This work focuses explicitly on using wavelet-based entropy methods within such systems, drawing upon examples from our previous research. These examples include evaluating cardiorespiratory dynamics through pneumocardiogram signal analysis, characterizing tumour and normal cell behaviour using entropy-based methods, analyzing low-amplitude seismic wave data for earthquake forecasting, and examining quantum fluctuations of fermionic instantons. These studies demonstrate the effectiveness of wavelet-based entropy methods in comprehending chaotic and complex systems. Furthermore, such approaches could enhance the predictability in seemingly disordered and complex social and economic interactions and international relations.