In this paper, we introduce a method called multiscale multifractal detrended cross-correlation analysis (MM-DCCA). The method allows us to extend the description of the cross-correlation properties between two time series. MM-DCCA may provide new ways of measuring the nonlinearity of two...

This paper presents a novel approach for the local analysis of the contour of a planar real world shape. The 1D representation of that contour is a very complicated signal with several non isolated and oscillating singularities, which represent the micro-structure of the shape. The analysis of...

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting...

Environmental assessment needs of Decision Support Systems (DSSs) able to consider several aspects in a unique analysis framework. The complexity of interaction among ecological, economic and political variables and a widespread lack of data availability lead to difficulty in bringing together...

In the realm of multiscale signal analysis, multifractal analysis provides a natural and rich framework to measure the roughness of a time series. As such, it has drawn special attention of both mathematicians and practitioners, and led them to characterize relevant physiological factors...

Life is one of the most complex nonlinear systems and heart is the core of this lifecycle system. Electrocardiogram (ECG) signals taken from healthy adult subjects have been found to characterize multifractality. In this paper, multiscale analysis method was introduced to find the most effective...