Fractal worlds with limited connectivity are the topological result of growing graphs from chaotic series. We show how this model presents original characteristics which cannot be detected by means of the standard network descriptors. In detail, intrinsic inaccessibility to the fully connected...

The Bandt–Pompe (BP) prescription for building up probability densities [C. Bandt, B. Pompe, Permutation entropy: a natural complexity measure for time series, Phys. Rev. Lett. 88 (2002) 174102] constituted a significant advance in the treatment of time-series. However, as we show here,...

The electricity system price of the Nord Pool spot market is analysed. Different time scale analysis tools are assessed with focus on the Hurst exponent and long range correlations. Daily and weekly periodicities of the spot market are identified. Even though space time separation plots suggest...

In this paper, we apply the principles of information theory that relate to the definition of nonlinear predictability, which is a measure that describes both the linear and nonlinear components of a system. By comparing this measure to a measure of linear predictability, one can assess whether...

In a previous paper (Physica A 312 (2002) 458) we presented an analysis, which established that DNA sequences exhibit significant nonlinear structure. More specifically, we found that both coding and non-coding sequences exhibit nonlinearity, but coding sequences are more nonlinear than...

We analyze, by means of the nonparametric estimation of the hazard function, the distribution of the earthquake occurrences in two regions of Spain. First, we present the method of estimation and the framework, and next we compare the seismic risk in the region of Galicia (northwest of Spain)...

This paper provides a nonparametric model of multi-step ahead forecasting in diffusion processes. The model is constructed from the local linear model with the Gaussian kernel. The paper provides simulation studies to evaluate its performance of multi-step ahead forecasting by comparing with the...

Exponential random graph models (ERGMs) are powerful tools for formulating theoretical models of network generation or learning the properties of empirical networks. They can be used to construct models that exactly reproduce network properties of interest. However, tuning these models correctly...