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

Modelagem e previsão da perda de injetividade em poços canhoneados

Waterflooding is a technique largely applied in the oil industry. The injected water displaces oil to the producer wells and avoid reservoir pressure decline. However, suspended particles in the injected water may cause plugging of pore throats causing formation damage (permeability reduction) and injectivity decline during waterflooding. When injectivity decline occurs it is necessary to increase the injection pressure in order to maintain water flow injection. Therefore, a reliable prediction of injectivity decline is essential in waterflooding projects. In this dissertation, a simulator based on the traditional porous medium filtration model (including deep bed filtration and external filter cake formation) was developed and applied to predict injectivity decline in perforated wells (this prediction was made from history data). Experimental modeling and injectivity decline in open-hole wells is also discussed. The injectivity of modeling showed good agreement with field data, which can be used to support plan stimulation injection wells

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Previsão do módulo de elasticidade transversal de compósitos unidirecionais através de redes neurais mistas

The aim of this study is to create an artificial neural network (ANN) capable of modeling the transverse elasticity modulus (E2) of unidirectional composites. To that end, we used a dataset divided into two parts, one for training and the other for ANN testing. Three types of architectures from different networks were developed, one with only two inputs, one with three inputs and the third with mixed architecture combining an ANN with a model developed by Halpin-Tsai. After algorithm training, the results demonstrate that the use of ANNs is quite promising, given that when they were compared with those of the Halpín-Tsai mathematical model, higher correlation coefficient values and lower root mean square values were observed

Análise de risco à erosão costeira na região de Tibau do Sul/RN através de mapeamento geoambiental e análise morfodinâmica

The research area is located on the county of Tibau do Sul, in the east coast of Rio Grande do Norte State, about 80km south of the capital Natal. The tourism represents the main income activity and Pipa beach is the most visited beach in the city, annually receives a large influx of domestic and foreign tourists. Some recent studies have reported the occurrence of coastal erosion in this littoral, being the main objective of the research, analyze the existing coastal erosion, through two methodologies, the geoenvironmental mapping and beach morphodynamics. The geoenvironmental mapping was done from oblique aerial photographs and field visits, which sought to carry out first the geomorphological mapping, with the purpose of analyzing features that suggest susceptible areas to erosion, as areas without protection of natural dunes, marine terraces, or sandstones (beach-rocks and ferruginous sandstones), areas with the presence of gullies and stretches where the sea-cliffs were in direct contact with the action of the sea, representing the beginning of the beach profile. In the morphodynamic study sought to carry out the survey of the physical and morphological characteristics, the analysis of sediment grain of the beaches and finally the analysis of the morphodynamic parameters to generate a table of risk to erosion by sector of the beach. The morphodynamic parameters were defined by the methodology proposed by Short (2006), in which considers different patterns of dynamism on beaches with characteristics favorable and unfavorable to erosive profiles. The maps indicated different levels of risk to the segments of the beaches analyzed, suggesting risk to erosion low and low to moderate only in areas north and northwest of the beaches of Madeiro and Curral, and levels of moderate and high risk sectors in the south and southeast of these beaches . The beach of Pipa showed moderate levels of risk and moderate to high at the ends and high risk to erosion in the central portion. The study of the coastal environment, its morphological evolution, and areas with problems of erosion, are of fundamental importance to assist coastal management policies, giving grants for planning activities undertaken in these regions

A erosão costeira e sua influência sobre a atividade petrolífera: alternativas sustentáveis na mitigação de impactos ambientais

The study area consist in high sensitivity environments located on the northern coast of Rio Grande do Norte, Northeast Brazil. The barrier island are the main geomorphological features on the coastal landscape, being naturally instable and surrounded by industrial activities like oil fields, salt industry, shrimp farms and urban areas sometimes installed parallel to the coast, combined with coast engineering interventions. High energy hydrodynamic process are responsible for the morphological instability of the coast. The study was based on remote sensing data obtained between 1954 and 2007 which consist in orbital images from Landsat, CBERS and Ikonos satellites and aerial photos. With all data integrated on GIS environment it was possible to update thematic maps of geology, geomorphology, vegetation, soil and landuse and development of multitemporal maps pointing areas with erosion and depositions of sediments, defining the critical erosion process on this region. The bigger morphological changes are related to changes on wind patterns during the year, terrestrial and sea breezes during the day, with spits and barrier island migration, opening and closing of channels like the one parallel to the coast on the area of Serra and Macau oil fields. These factors combined with the significant reduction on sediment budgets due to the loss of natural spaces to sediment reworking contribute to the low resilience which tends to be growing on the area of Serra and Macau oil fields. In front of such scenery a detailed monitoring was done in order to find technological possibilities for coastal restoration. A pilot area was defined to start the project of mangrove restore together with beach nourishment in order to minimize the effect of the erosion caused by the channel parallel to the coast, contributing to stabilize the northeast channel as the main one. It s expected that such methodology will aid the coastal environments restoration and the balance between industrial activities and coastal erosion

Estudo da previsão da carga de ruptura de estacas pré-moldadas de concreto

The piles are one of the most important types of solution adopted for the foundation of buildings. They are responsible for transmitting to the soil in deepe r and resistant layers loads from structures. The interaction of the foundation element with the soil is a very important variable, making indispensable your domain in order to determine the strength of the assembly and establish design criteria for each c ase of application of the pile. In this research analyzes were performed f rom experiments load tests for precast concrete piles and inve stigations of soil of type SPT, a study was performed for obtaining the ultimate load capacity of the foundation through methods extrapolation of load - settlement curve , semi - empirical and theoretic . After that, were realized comparisons between the different methods used for two types of soil a granular behavior and other cohesive. For obtaining soil paramet ers to be used i n the methods were established empirical correlations with the standard penetration number (NSPT). The charge - settlement curves of the piles are also analyzed. In the face of established comparisons was indicated the most reliable semiempirical method Déco urt - Quaresma as the most reliable for estimating the tensile strength for granular and cohesive soils. Meanwhile, among the methods studied extrapolation is recommended method of Van der Veen as the most appropriate for predicting the tensile strength.

Estimação e previsão no processo INARCH(2)

In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

Estimação e previsão no processo INARCH(2)

In the last decades the study of integer-valued time series has gained notoriety due to its broad applicability (modeling the number of car accidents in a given highway, or the number of people infected by a virus are two examples). One of the main interests of this area of study is to make forecasts, and for this reason it is very important to propose methods to make such forecasts, which consist of nonnegative integer values, due to the discrete nature of the data. In this work, we focus on the study and proposal of forecasts one, two and h steps ahead for integer-valued second-order autoregressive conditional heteroskedasticity processes [INARCH (2)], and in determining some theoretical properties of this model, such as the ordinary moments of its marginal distribution and the asymptotic distribution of its conditional least squares estimators. In addition, we study, via Monte Carlo simulation, the behavior of the estimators for the parameters of INARCH(2) processes obtained using three di erent methods (Yule- Walker, conditional least squares, and conditional maximum likelihood), in terms of mean squared error, mean absolute error and bias. We present some forecast proposals for INARCH(2) processes, which are compared again via Monte Carlo simulation. As an application of this proposed theory, we model a dataset related to the number of live male births of mothers living at Riachuelo city, in the state of Rio Grande do Norte, Brazil.

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

Modelagem e previsão da perda de injetividade em poços canhoneados

Waterflooding is a technique largely applied in the oil industry. The injected water displaces oil to the producer wells and avoid reservoir pressure decline. However, suspended particles in the injected water may cause plugging of pore throats causing formation damage (permeability reduction) and injectivity decline during waterflooding. When injectivity decline occurs it is necessary to increase the injection pressure in order to maintain water flow injection. Therefore, a reliable prediction of injectivity decline is essential in waterflooding projects. In this dissertation, a simulator based on the traditional porous medium filtration model (including deep bed filtration and external filter cake formation) was developed and applied to predict injectivity decline in perforated wells (this prediction was made from history data). Experimental modeling and injectivity decline in open-hole wells is also discussed. The injectivity of modeling showed good agreement with field data, which can be used to support plan stimulation injection wells

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior