16,907 research outputs found
Time series irreversibility: a visibility graph approach
We propose a method to measure real-valued time series irreversibility which
combines two differ- ent tools: the horizontal visibility algorithm and the
Kullback-Leibler divergence. This method maps a time series to a directed
network according to a geometric criterion. The degree of irreversibility of
the series is then estimated by the Kullback-Leibler divergence (i.e. the
distinguishability) between the in and out degree distributions of the
associated graph. The method is computationally effi- cient, does not require
any ad hoc symbolization process, and naturally takes into account multiple
scales. We find that the method correctly distinguishes between reversible and
irreversible station- ary time series, including analytical and numerical
studies of its performance for: (i) reversible stochastic processes
(uncorrelated and Gaussian linearly correlated), (ii) irreversible stochastic
pro- cesses (a discrete flashing ratchet in an asymmetric potential), (iii)
reversible (conservative) and irreversible (dissipative) chaotic maps, and (iv)
dissipative chaotic maps in the presence of noise. Two alternative graph
functionals, the degree and the degree-degree distributions, can be used as the
Kullback-Leibler divergence argument. The former is simpler and more intuitive
and can be used as a benchmark, but in the case of an irreversible process with
null net current, the degree-degree distribution has to be considered to
identifiy the irreversible nature of the series.Comment: submitted for publicatio
The role of bot squads in the political propaganda on Twitter
Social Media are nowadays the privileged channel for information spreading
and news checking. Unexpectedly for most of the users, automated accounts, also
known as social bots, contribute more and more to this process of news
spreading. Using Twitter as a benchmark, we consider the traffic exchanged,
over one month of observation, on a specific topic, namely the migration flux
from Northern Africa to Italy. We measure the significant traffic of tweets
only, by implementing an entropy-based null model that discounts the activity
of users and the virality of tweets. Results show that social bots play a
central role in the exchange of significant content. Indeed, not only the
strongest hubs have a number of bots among their followers higher than
expected, but furthermore a group of them, that can be assigned to the same
political tendency, share a common set of bots as followers. The retwitting
activity of such automated accounts amplifies the presence on the platform of
the hubs' messages.Comment: Under Submissio
A combinatorial framework to quantify peak/pit asymmetries in complex dynamics
LL’s acknowledges funding from an EPSRC Early Career Fellowship EP/P01660X/1
Self-Evaluation Applied Mathematics 2003-2008 University of Twente
This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008
Investigation of Causality Pattern Structure for the Exploration of Dynamic Time-Varying Behaviour
The analysis of time-varying interactions within multivariate systems has seen a great deal of interest within the last decade, with the international oil market being an archetypal and important system that demonstrates this behaviour. However, unlike work on static systems, research on time-varying systems rarely leverages specific information on the inter-system interactions for understanding the systems temporal dynamics. This thesis utilises this information to present methodologies for new descriptions of these systems, focussing on the international oil market. This is achieved via three experiments.
The first experiment expands upon the state-of-the-art methodologies for investigating these systems; complex networks. Presenting a novel complex network approach that encodes the transitional behaviour of the dynamic interactions. The work introduces: two transition metrics, a complex network, and various metrics and properties of this network. Using this approach it is shown that for the international oil market the evolution favours staying in similar causality patterns before switching to a new group of similar patterns.
The second experiment puts forth two novel paradigms for the evolution of a dynamic multivariate system, and from these paradigms the principle features that drive the systems dynamics. It is also shown demonstrated that a p-value representation of causality can improve the description of the dynamics. Through dimensional reduction based on these paradigms and prediction of the systems future states on the reduced system, that the international oil market dynamics are well captured by the total change in causality of the system.
The third experiment further explores and validates a hypothesis of the international oil markets dynamics based on the findings of the first two experiments. Proposing a approach for the formal definition of such system dynamics, and applying this to the proposed hypothesis. This hypothesis is then validated via a novel clustering approaches to determine that the international oil markets state is primarily contained within clusters that slightly vary around central causality patterns, and that the system does not follow a repeated gradual change when transitioning between these clusters.
This work allows for a more detailed and alternative description of a system's dynamic behaviour than those given by other current methodologies
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