3 accepted papers
The aim of this work is to investigate the ability of deep learning (DL) architectures to learn temporal dynamics in multivariate time series. The methodology consists in using well known synthetic stochastic processes for which changes in joint temporal dynamics can be controlled. This permits to c…
Multifractal analysis, notably with its recent wavelet-leader based formulation, has nowadays become a reference tool to characterize scale-free temporal dynamics in time series. It proved successful in numerous applications very diverse in nature. However, such successes remained restricted to univ…
Among many research efforts devoted to automated art investigations, the problem of quantification of literary style remains current. Meanwhile, linguists and computer scientists have tried to sort out texts according to their types or authors. We use the recently-introduced p-leader multifractal fo…