Marinho Gomes Andrade

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A map of multi-dimensional data is a graphical representation - defined in 2D or 3D space - of a set of data points that reflects similarity relationships amongst them. Triangulations of those points can be produced to generate surface meshes on which additional information can be mapped to visual attributes such as color or height. Such surfaces may then(More)
Time series analysis poses many challenges to professionals in a wide range of domains. Several visualization solutions integrated with mining algorithms have been proposed for exploratory tasks on time series collections. As the data sets grow large, though, the visual alternatives do not allow for a good association between similar time series. In this(More)
Time series analysis poses many challenges to professionals in a wide range of domains. Several visualization solutions have been proposed for exploratory tasks on time series collections. For large data sets, however, current techniques fail to provide a global view that supports a good association between groups of similar time series. We employ fast(More)
In this paper, a Bayesian method for inference is developed for the zero-modified Poisson (ZMP) regression model. This model is very flexible for analyzing count data without requiring any information about inflation or deflation of zeros in the sample. A general class of prior densities based on an information matrix is considered for the model parameters.(More)
The reaction of nickel (II) with Br-PADAP, in the presence of tergitol NPX surfactant, forms a complex with absorption peaks at 520 and 560 nm. The iron(II)-Br-PADAP system at the same conditions forms a chelate with absorption peaks at 560 and 748 nm. This allows the simultaneous spectrophotometric determination of nickel and iron by measuring the(More)
In this work we study the problem of modeling identification of a population employing a discrete dynamic model based on the Richards growth model. The population is subjected to interventions due to consumption, such as hunting or farming animals. The model identification allows us to estimate the probability or the average time for a population number to(More)
The aims of this paper are estimate and forecast the Non-Accelerating Inflation Rate of Unemployment, or nairu, for Brazilian unemployment time series data. In doing so, we introduce a methodology for estimating mixed additive seasonal autoregressive (masar) models, by the Generalized Method of Moments (gmm). Furthermore, in order to cover a lack in(More)
This paper presents a neural fuzzy network model for seasonal streamflow forecasting. The model is based on a constructive learning method where neurons groups compete when the network receives a new input, so that it learns the fuzzy rules and membership functions essential for modelling a fuzzy system. The model was applied to the problem of seasonal(More)