Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model

Identificação de modelos polinomiais narx utilizando algoritmos combinados de detecção de estrutura e estimação de parâmetros com aplicações práticas

A modelagem de processos industriais tem auxiliado na produção e minimização de custos, permitindo a previsão dos comportamentos futuros do sistema, supervisão de processos e projeto de controladores. Ao observar os benefícios proporcionados pela modelagem, objetiva-se primeiramente, nesta dissertação, apresentar uma metodologia de identificação de modelos não-lineares com estrutura NARX, a partir da implementação de algoritmos combinados de detecção de estrutura e estimação de parâmetros. Inicialmente, será ressaltada a importância da identificação de sistemas na otimização de processos industriais, especificamente a escolha do modelo para representar adequadamente as dinâmicas do sistema. Em seguida, será apresentada uma breve revisão das etapas que compõem a identificação de sistemas. Na sequência, serão apresentados os métodos fundamentais para detecção de estrutura (Modificado Gram- Schmidt) e estimação de parâmetros (Método dos Mínimos Quadrados e Método dos Mínimos Quadrados Estendido) de modelos. No trabalho será também realizada, através dos algoritmos implementados, a identificação de dois processos industriais distintos representados por uma planta de nível didática, que possibilita o controle de nível e vazão, e uma planta de processamento primário de petróleo simulada, que tem como objetivo representar um tratamento primário do petróleo que ocorre em plataformas petrolíferas. A dissertação é finalizada com uma avaliação dos desempenhos dos modelos obtidos, quando comparados com o sistema. A partir desta avaliação, será possível observar se os modelos identificados são capazes de representar as características estáticas e dinâmicas dos sistemas apresentados nesta dissertação

Construção de modelos multivariados para determinação de lipídios totais e unidade em leite em pó comercial utilizando espectroscopia no infravermelho próximo

In this work calibration models were constructed to determine the content of total lipids and moisture in powdered milk samples. For this, used the near-infrared spectroscopy by diffuse reflectance, combined with multivariate calibration. Initially, the spectral data were submitted to correction of multiplicative light scattering (MSC) and Savitzsky-Golay smoothing. Then, the samples were divided into subgroups by application of hierarchical clustering analysis of the classes (HCA) and Ward Linkage criterion. Thus, it became possible to build regression models by partial least squares (PLS) that allowed the calibration and prediction of the content total lipid and moisture, based on the values obtained by the reference methods of Soxhlet and 105 ° C, respectively . Therefore, conclude that the NIR had a good performance for the quantification of samples of powdered milk, mainly by minimizing the analysis time, not destruction of the samples and not waste. Prediction models for determination of total lipids correlated (R) of 0.9955, RMSEP of 0.8952, therefore the average error between the Soxhlet and NIR was ± 0.70%, while the model prediction to content moisture correlated (R) of 0.9184, RMSEP, 0.3778 and error of ± 0.76%

Conciliação entre modelos de mecanismos avançados de oxidação eletroquímica

Many pollutants dumped in waterways, such as dyes and pesticides, have become so ubiquitous that they represent a serious threat to human health. The electrochemical oxidation is presented as an alternative clean, efficient and economic degradation of wastewater containing organic compounds and a number of advantages of this technique is to just not make use of chemical reagents, since only electrical energy is consumed during the removal of pollutants organic. However, despite being a promising alternative, still needs some tweaking in order to obtain better efficiency in the elimination of persistent pollutants. Thus, this study sought a relationship between a recently discovered phenomenon that reflects the participation of dissolved oxygen in solution in the electrochemical oxidation process, as an anomaly, present a kinetic model that shows instantaneous current efficiency (ICE) above 100% limited by theory, manifested for some experiments with phenolic compounds with H2SO4 or HClO4 as supporting electrolyte with electrodes under anodic oxidation on boron doped diamond (BDD). Therefore it was necessary to reproduce the data ICE exposes the fault model, and thus the 2-naphthol was used as phenolic compound to be oxidised at concentrations of 9, 12 and 15 mmol L-1, and H2SO4 and HClO4 to 1 mol L-1 as a supporting electrolyte under a current density of 30 mA cm-2 in an electrochemical reactor for continuous flow disk configuration, and equipped with anodes DDB at room temperature (25 oC). Experiments were performed using N2 like as purge gas for eliminate oxygen dissolved in solution so that its influence in the system was studied. After exposure of the anomaly of the ICE model and investigation of its relationship with dissolved O2, the data could be treated, making it possible for confirmation. But not only that, the data obtained from eletranálise and spectroscopic analysis suggest the involvement of other strongly oxidizing species (O3 (ozone) and O radicals and O2 -), since the dissolved O2 can be consumed during the formation of new strong oxidizing species, not considered until now, something that needs to be investigated by more accurate methods that we may know a little more of this system. Currently the performance of the electrocatalytic process is established by a complex interaction between different parameters that can be optimized, so it is necessary to the implementation of theoretical models, which are the conceptual lens with which researchers see

Desempenho de analistas sell-side no mercado de ações brasileiro

The purpose of this study was to analyze the behavior of Sell-Side analysts and analysts propose a classification, considering the performance of the price forecasts and recom- mendations (sell-hold-buy) in the Brazilian stock market. For this, the first step was to analyze the consensus of analysts to understand the importance of this collective interven- tion in the market; the second was to analyze the analysts individually to understand how improve their analysis in time. Third was to understand how are the main methods of ranking used in markets. Finally, propose a form of classification that reflects the previous aspects discussed. To investigate the hypotheses proposed in the study were used linear models for panel to capture elements in time. The data of price forecasts and analyst recommendations individually and consensus, in the period 2005-2013 were obtained from Bloomberg R ○ . The main results were: (i) superior performance of consensus recommen- dations, compared with the individual analyzes; (ii) associating the number of analysts issuing recommendations with improved accuracy allows supposing that this number may be associated with increased consensus strength and hence accuracy; (iii) the anchoring effect of the analysts consensus revisions makes his predictions are biased, overvaluating the assets; (iv) analysts need to have greater caution in times of economic turbulence, noting also foreign markets such as the USA. For these may result changes in bias between optimism and pessimism; (v) effects due to changes in bias, as increased pessimism can cause excessive increase in purchase recommendations number. In this case, analysts can should be more cautious in analysis, mainly for consistency between recommendation and the expected price; (vi) the experience of the analyst with the asset economic sector and the asset contributes to the improvement of forecasts, however, the overall experience showed opposite evidence; (vii) the optimism associated with the overall experience, over time, shows a similar behavior to an excess of confidence, which could cause reduction of accuracy; (viii) the conflicting effect of general experience between the accuracy and the observed return shows evidence that, over time, the analyst has effects similar to the endowment bias on assets, which would result in a conflict analysis of recommendations and forecasts ; (ix) despite the focus on fewer sectors contribute to the quality of accuracy, the same does not occur with the focus on assets. So it is possible that analysts may have economies of scale when cover more assets within the same industry; and finally, (x) was possible to develop a proposal for classification analysts to consider both returns and the consistency of these predictions, called Analysis coefficient. This ranking resulted better results, considering the return / standard deviation.

Estudo do balanço de umidade por meio de modelos regionais para o clima do passado e do futuro sobre a América do Sul

In the context of climate change over South America (SA) has been observed that the combination of high temperatures and rain more temperatures less rainfall, cause different impacts such as extreme precipitation events, favorable conditions for fires and droughts. As a result, these regions face growing threat of water shortage, local or generalized. Thus, the water availability in Brazil depends largely on the weather and its variations in different time scales. In this sense, the main objective of this research is to study the moisture budget through regional climate models (RCM) from Project Regional Climate Change Assessments for La Plata Basin (CLARIS-LPB) and combine these RCM through two statistical techniques in an attempt to improve prediction on three areas of AS: Amazon (AMZ), Northeast Brazil (NEB) and the Plata Basin (LPB) in past climates (1961-1990) and future (2071-2100). The moisture transport on AS was investigated through the moisture fluxes vertically integrated. The main results showed that the average fluxes of water vapor in the tropics (AMZ and NEB) are higher across the eastern and northern edges, thus indicating that the contributions of the trade winds of the North Atlantic and South are equally important for the entry moisture during the months of JJA and DJF. This configuration was observed in all the models and climates. In comparison climates, it was found that the convergence of the flow of moisture in the past weather was smaller in the future in various regions and seasons. Similarly, the majority of the SPC simulates the future climate, reduced precipitation in tropical regions (AMZ and NEB), and an increase in the LPB region. The second phase of this research was to carry out combination of RCM in more accurately predict precipitation, through the multiple regression techniques for components Main (C.RPC) and convex combination (C.EQM), and then analyze and compare combinations of RCM (ensemble). The results indicated that the combination was better in RPC represent precipitation observed in both climates. Since, in addition to showing values be close to those observed, the technique obtained coefficient of correlation of moderate to strong magnitude in almost every month in different climates and regions, also lower dispersion of data (RMSE). A significant advantage of the combination of methods was the ability to capture extreme events (outliers) for the study regions. In general, it was observed that the wet C.EQM captures more extreme, while C.RPC can capture more extreme dry climates and in the three regions studied.

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model