Progress in combustion science and engineering has led to the generation of large amounts
of data from large-scale simulations, high-resolution experiments, and sensors. This corpus …

In the last decade, there has been a growing body of literature addressing the utilization of
complex network methods for the characterization of dynamical systems based on time …

In recent years a new type of tradable assets appeared, generically known as
cryptocurrencies. Among them, the most widespread is Bitcoin. Given its novelty, this paper …

Entropy is a powerful tool for the analysis of time series, as it allows describing the
probability distributions of the possible state of a system, and therefore the information …

The visibility algorithm has been recently introduced as a map** between time series and
complex networks. This procedure allows us to apply methods of complex network theory for …

Machine learning has become a widely popular and successful paradigm, especially in data-
driven science and engineering. A major application problem is data-driven forecasting of …

Blockchain technology, and more specifically bitcoin (one of its foremost applications), have
been receiving increasing attention in the scientific community. The first publications with …

We introduce a transformation from time series to complex networks and then study the
relative frequency of different subgraphs within that network. The distribution of subgraphs …

One of the most important aspects of time series is their degree of stochasticity vs. chaoticity.
Since the discovery of chaotic maps, many algorithms have been proposed to discriminate …

Learning from successful applications of methods originating in statistical mechanics,
complex systems science, or information theory in one scientific field (eg, atmospheric …