arXiv subjects

Ricardo Mansilla

Publications and source records attributed to Ricardo Mansilla.

13 recordsLinked to original sources

arXiv2023

Discrete Gompertz and Generalized Logistic Models for early monitoring of the COVID-19 pandemic in Cuba

For the last few years there has been a resurgence in the use of phenomenological growth models for predicting the early dynamics of infectious diseases. These models assume that time is a continuous variable whereas in the present contribution, the discrete versions of Gompertz and Generalized Logistic models are used for early monitoring and short-term forecasting of the spread of an epidemic in a region. The time-continuous models are represented mathematically by first-order differential equations while their discrete versions are represented by first-order difference equations that involve parameters that should be estimated prior to forecasting. The methodology for estimating such parameters is described in detail. Real data of COVID-19 infection in Cuba is used to illustrate this methodology. The proposed methodology was implemented for the first thirty-five days, being able to predict with very good precision the data reported for the following twenty days. The codes implemented to study the Gompertz model in differences are included in an appendix with each step of the methodology identified.

q-bio.PE↗

arXiv2022

Ordinal Synchronization and Typical States in High-Frequency Digital Markets

In this paper we study Algorithmic High-Frequency Financial Markets as dynamical networks. After an individual analysis of 24 stocks of the US market during a trading year of fully automated transactions by means of ordinal pattern series, we define an information-theoretic measure of pairwise synchronization for time series which allows us to study this subset of the US market as a dynamical network. We apply to the resulting network a couple of clustering algorithms in order to detect collective market states, characterized by their degree of centralized or descentralized synchronicity. This collective analysis has shown to reproduce, classify and explain the anomalous behavior previously observed at the individual level. We also find two whole coherent seasons of highly centralized and descentralized synchronicity, respectively. Finally, we model these states dynamics through a simple Markov model.

nlin.AO↗

arXiv2021

Analyzing time series activity of Twitter political spambots

The presence and complexity of political Twitter bots has increased in recent years, making it a very difficult task to recognize these accounts from real, human users. We intended to provide an answer to the following question: are temporal patterns of activity qualitatively different in fake and human accounts? We collected a large sample of tweets during the post-electoral conflict in the US in 2020 and performed supervised and non-supervised statistical learning technique sto quantify the predictive power of time-series features for human-bot recognition. Our results show that there are no substantial differences, suggesting that political bots are nowadays very capable of mimicking human behaviour. This finding reveals the need for novel, more sophisticated bot-detection techniques.

cs.SI↗

arXiv2020

Modeling the Popularity of Twitter Hashtags with Master Equations

In this work we introduce a model based on master equations to describe the time evolution of the popularity of topics and hashtags on the Twitter social network. Specifically, we model the number of times a certain hashtag appears on the network as a function of time. In our model, the behavior of this quantity depends on the degree distribution of the network and the extrinsic interest the community has for the topic or hashtag. From the master equation, we are able to obtain explicit solutions for the mean and variance. We propose a gamma kernel function to model the topic popularity, which is quite simple and yields reasonable results. Finally, we validate the plausibility of the model by analyzing actual Twitter data obtained through the public API.

cs.SI↗

arXiv2020

Analysis of intra-day fluctuations in the Mexican financial market index

In this paper, a statistical analysis of high frequency fluctuations of the IPC, the Mexican Stock Market Index, is presented. A sample of tick-to-tick data covering the period from January 1999 to December 2002 was analyzed, as well as several other sets obtained using temporal aggregation. Our results indicates that the highest frequency is not useful to understand the Mexican market because almost two thirds of the information corresponds to inactivity. For the frequency where fluctuations start to be relevant, the IPC data does not follows any alpha-stable distribution, including the Gaussian, perhaps because of the presence of autocorrelations. For a long range of lower-frequencies, but still in the intra-day regime, fluctuations can be described as a truncated Lévy flight, while for frequencies above two-days, a Gaussian distribution yields the best fit. Thought these results are consistent with other previously reported for several markets, there are significant differences in the details of the corresponding descriptions.

q-fin.ST↗

arXiv2019

Distribuciones de probabilidad en las ciencias de la complejidad: una perspectiva contemporánea

Science in the 21st century seems to be governed by novel approaches involving interdisciplinary work, systemic perspectives and complexity theory concepts. These new paradigms force us to leave aside our elder mechanistic approaches and embrace new starting points based on stochasticity, chaoticity, statistics and probability. In this work we review the fundamental ideas of complexity theory and the classic probabilistic models to study complex systems, based on the law of large numbers, central limit theorems and stable distributions. We also talk about power laws as the most common model for phenomena showing long tail distributions and we explore the principal difficulties that arise in practice with this kind of models. We show a novel alternative for the descripition of this type of phenomena and lastly we show two examples that illustrate the applications of this new model.

physics.soc-ph↗

arXiv2019

Beta Rank Function: A Smooth Double-Pareto-Like Distribution

The Beta Rank Function (BRF) $x(u) =A(1-u)^b/u^a$, where $u$ is the normalized and continuous rank of an observation $x$, has wide applications in fitting real-world data from social science to biological phenomena. The underlying probability density function (pdf) $f_X(x)$ does not usually have a closed expression except for specific parameter values. We show however that it is approximately a unimodal skewed and asymmetric two-sided power law/double Pareto/log-Laplacian distribution. The BRF pdf has simple properties when the independent variable is log-transformed: $f_{Z=\log(X)}(z)$ . At the peak it makes a smooth turn and it does not diverge, lacking the sharp angle observed in the double Pareto or Laplace distribution. The peak position of $f_Z(z)$ is $z_0=\log A+(a-b)\log(\sqrt{a}+\sqrt{b})-(a\log(a)-b\log(b))/2 $; the probability is partitioned by the peak to the proportion of $\sqrt{b}/(\sqrt{a}+\sqrt{b})$ (left) and $\sqrt{a}/(\sqrt{a}+\sqrt{b})$ (right); the functional form near the peak is controlled by the cubic term in the Taylor expansion when $a\ne b$; the mean of $Z$ is $E[Z]=\log A+a-b$; the decay on left and right sides of the peak is approximately exponential with forms $e^{\frac{z-\log A}{b} }/b$ and $e^{ -\frac{z-\log A}{a}}/a$. These results are confirmed by numerical simulations. Properties of $f_X(x)$ without log-transforming the variable are much more complex, though the approximate double Pareto behavior, $(x/A)^{1/b}/(bx)$ (for $x A$) is simple. Our results elucidate the relationship between BRF and log-normal distributions when $a=b$ and explain why the BRF is ubiquitous and versatile. Based on the pdf, we suggest a quick way to elucidate if a real data set follows a one-sided power-law, a log-normal, a two-sided power-law or a BRF. We illustrate our results with two examples: urban populations and financial returns.

stat.ME↗

arXiv2011

On the scaling of the distribution of daily price fluctuations in Mexican financial market index

In this paper, a statistical analysis of log-return fluctuations of the IPC, the Mexican Stock Market Index is presented. A sample of daily data covering the period from $04/09/2000-04/09/2010$ was analyzed, and fitted to different distributions. Tests of the goodness of fit were performed in order to quantitatively asses the quality of the estimation. Special attention was paid to the impact of the size of the sample on the estimated decay of the distributions tail. In this study a forceful rejection of normality was obtained. On the other hand, the null hypothesis that the log-fluctuations are fitted to a $α$-stable Lévy distribution cannot be rejected at 5% significance level.

q-fin.ST↗

arXiv2000

Stewart-Lyth Inverse Problem

In this paper the Stewart-Lyth inverse problem is introduced. It consists of solving two non-linear differential equations for the first slow-roll parameter and finding the inflaton potential. The equations are derived from the Stewart--Lyth equations for the scalar and tensorial perturbations produced during the inflationary period. The geometry of the phase planes transverse to the trajectories is analyzed, and conclusions about the possible behaviour for general solutions are drawn.

astro-ph↗

arXiv2000

On the Stewart-Lyth Inverse Problem

In this paper the Stewart-Lyth inverse problem is rewritten using the comoving scales as the basic parameter. It is shown that some information on the inflaton potential can be obtained from observations taking into account only the scalar power spectrum.

astro-ph↗

arXiv2000

Algorithmic Complexity of Real Financial Markets

A new approach to the understanding of the complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a magnitude rooted in the Kolmogorov-Chaitin theory is applied to binary sequences built up from real time series of financial markets indices. The study is based on NASDAQ and Mexican IPC data. Different behaviors of this magnitude are shown when applied to the intervals of series placed before crashes and in intervals when no financial turbulence is observed. The connection between our results and The Efficient Market Hypothesis is discussed.

cond-mat.stat-mech↗

arXiv2000

Deterministic site exchange cellular automata model for the spread of diseases in human settlements

A cellular automata model that describes as limit cases of his parameters the spread of contagious diseases modeled by systems of ordinary or partial differential equations is developed. Periodic features of the behavior of human settlement are considered. The model is built taking into account the range of motion of the elements of population. For small (large) values of this range, the behaviors described by partial (ordinary) differential equation models are reproduced. Emphasis is done in the study of those scenarios in which the above mentioned equations fail to describe. Some interesting results in these cases are reported.

nlin.CG↗

arXiv2000

From naive to sophisticated behavior in multiagents based financial market models

We discuss the behavior of two magnitudes, physical complexity and mutual information function of the outcome of a model of heterogeneous, inductive rational agents inspired in the El Farol Bar problem and the Minority Game. The first is a measure rooted in Kolmogorov-Chaitin theory and the second one a measure related with information entropy of Shannon. We make extensive computer simulations, as result of which, we propose an ansatz for physical complexity and establish the dependence of exponent of that ansatz from the parameters of the model. We discuss the accuracy of our results and the relationship with the behavior of mutual information function as a measure of time correlations of agents choice.

cond-mat.stat-mech↗