Ricardo S. Ehlers

Combining time series prediction models using genetic algorithm to autoscaling Web applications hosted in the cloud infrastructure

In a cloud computing environment, companies have the ability to allocate resources according to demand. However, there is a delay that may take minutes between the request for a new resource and it being ready for using. This causes the reactive techniques, which request a new resource only when the system reaches a certain load threshold, to be not suitable for the resource allocation process. To address this problem, it is necessary to predict requests that arrive at the system in the next period of time to allocate the necessary resources, before the system becomes overloaded. There are several time series forecasting models to calculate the workload predictions based on history of monitoring data. However, it is difficult to know which is the best time series forecasting model to be used in each case. The work becomes even more complicated when the user does not have much historical data to be analyzed. Most related work considers only single methods to evaluate the results of the forecast. Other works propose an approach that selects suitable forecasting methods for a given context. But in this case, it is necessary to have a significant amount of data to train the classifier. Moreover, the best solution may not be a specific model, but rather a combination of models. In this paper we propose an adaptive prediction method using genetic algorithms to combine time series forecasting models. Our method does not require a previous phase of training, because it constantly adapts the extent to which the data are coming. To evaluate our proposal, we use three logs extracted from real Web servers. The results show that our proposal often brings the best result and is generic enough to adapt to various types of time series.

Bayesian multivariate GARCH models with dynamic correlations and asymmetric error distributions

The main goal in this paper is to develop and apply stochastic simulation techniques for GARCH models with multivariate skewed distributions using the Bayesian approach. Both parameter estimation and model comparison are not trivial tasks and several approximate and computationally intensive methods (Markov chain Monte Carlo) will be used to this end. We consider a flexible class of multivariate distributions which can model both skewness and heavy tails. Also, we do not fix tail behaviour when dealing with fat tail distributions but leave it subject to inference.

A Weibull Mixture Model for the Votes of a Brazilian Political Party

Statistical modeling in political analysis is used recently to describe electoral behaviour of political party. In this chapter we propose a Weibull mixture model that describes the votes obtained by a political party in Brazilian presidential elections. We considered the votes obtained by the Workers’ Party in five presidential elections from 1994 to 2010. A Bayesian approach was considered and a random walk Metropolis algorithm within Gibbs sampling was implemented. Next, Bayes factor was considered to the choice of the number of components in the mixture. In addition the probability of obtain 50 % of the votes in the first round was estimated. The results show that only few components are needed to describe the votes obtained in this election. Finally, we found that the probability of obtaining 50 % of the votes in the first ballot is increasing along time. Future developments are discussed.

Comparison of Bayesian models for production efficiency

In this paper, we use Markov Chain Monte Carlo (MCMC) methods in order to estimate and compare stochastic production frontier models from a Bayesian perspective. We consider a number of competing models in terms of different production functions and the distribution of the asymmetric error term. All MCMC simulations are done using the package JAGS (Just Another Gibbs Sampler), a clone of the classic BUGS package which works closely with the R package where all the statistical computations and graphics are done.

A Study of Skewed Heavy-Tailed Distributions as Scale Mixtures

SYNOPTIC ABSTRACT In this article, we study and compare different proposals of heavy-tailed (possibly skewed) distributions as robust alternatives to the normal model. The density functions are all represented as scale mixtures, which enables efficient Bayesian estimation via Markov chain Monte Carlo (MCMC) methods. However, although the symmetric versions of these distributions are able to model heavy tails, they of course fail to capture asymmetry; for example, when the dataset contains extreme values in one of the tails. Therefore, distributions that accommodate skewness, as well as fat tails, are taken into account.

Original article: Computational tools for comparing asymmetric GARCH models via Bayes factors

In this paper we use Markov chain Monte Carlo (MCMC) methods in order to estimate and compare GARCH models from a Bayesian perspective. We allow for possibly heavy tailed and asymmetric distributions in the error term. We use a general method proposed in the literature to introduce skewness into a continuous unimodal and symmetric distribution. For each model we compute an approximation to the marginal likelihood, based on the MCMC output. From these approximations we compute Bayes factors and posterior model probabilities.

Bayesian Estimation of the Kumaraswamy InverseWeibull Distribution

The Kumaraswamy Inverse Weibull distribution has the ability to model failure rates that have unimodal shapes and are quite common in reliability and biological studies. The three-parameter Kumaraswamy Inverse Weibull distribution with decreasing and unimodal failure rate is introduced. We provide a comprehensive treatment of the mathematical properties of the Kumaraswany Inverse Weibull distribution and derive expressions for its moment generating function and the

Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo

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Riemann manifold Langevin methods on stochastic volatility estimation

-th generalized moment. Some properties of the model with some graphs of density and hazard function are discussed. We also discuss a Bayesian approach for this distribution and an application was made for a real data set.

Efficient Resource Allocation for Web Applications Hosted in the Cloud by means of Weighted Multi-objective Linear Programming

ABSTRACT In this article, we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian approach using traditional Metropolis-Hastings methods, Hamiltonian Monte Carlo (HMC), and Riemann manifold HMC (RMHMC) methods to obtain the approximations to the posterior marginal distributions of interest. Applications to real datasets and simulation studies provide evidence that the extra analytical work involved in Hamiltonian Monte Carlo algorithms is compensated by a more efficient exploration of the parameter space.