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

Dinâmica das configurações de formação e inibição das chuvas no Rio Grande do Norte: caracterização hidroclimática do estado

This study aimed to evaluate the influence of the main meteorological mechanisms trainers and inhibitors of precipitation, and the interactions between different scales of operation, the spatial and temporal variability of the annual cycle of precipitation in the Rio Grande do Norte. Além disso, considerando as circunstâncias locais e regionais, criando assim uma base científica para apoiar ações futuras na gestão da demanda de água no Estado. Database from monthly precipitation of 45 years, ranging between 1963 and 2007, data provided by EMPARN. The methodology used to achieve the results was initially composed of descriptive statistical analysis of historical data to prove the stability of the series, were applied after, geostatistics tool for plotting maps of the variables, within the geostatistical we opted for by Kriging interpolation method because it was the method that showed the best results and minor errors. Among the results, we highlight the annual cycle of rainfall the State which is influenced by meteorological mechanisms of different spatial and temporal scales, where the main mechanisms cycle modulators are the Conference Intertropical Zone (ITCZ) acting since midFebruary to mid May throughout the state, waves Leste (OL), Lines of instability (LI), breeze systems and orographic rainfall acting mainly in the Coastal strip between February and July. Along with vortice of high levels (VCANs), Complex Mesoscale Convective (CCMs) and orographic rain in any region of the state mainly in spring and summer. In terms of larger scale phenomena stood out El Niño and La Niña, ENSO in the tropical Pacific basin. In La Niña episodes usually occur normal or rainy years, as upon the occurrence of prolonged periods of drought are influenced by EL NIÑO. In the Atlantic Ocean the standard Dipole also affects the intensity of the rainfall cycle in State. The cycle of rains in Rio Grande do Norte is divided into two periods, one comprising the regions West, Central and the Western Portion of the Wasteland Potiguar mesoregions of west Chapada Borborema, causing rains from midFebruary to mid-May and a second period of cycle, between February-July, where rains occur in mesoregions East and of the Wasteland, located upwind of the Chapada Borborema, both interspersed with dry periods without occurrence of significant rainfall and transition periods of rainy - dry and dry-rainy where isolated rainfall occur. Approximately 82% of the rainfall stations of the state which corresponds to 83.4% of the total area of Rio Grande do Norte, do not record annual volumes above 900 mm. Because the water supply of the State be maintained by small reservoirs already are in an advanced state of eutrophication, when the rains occur, act to wash and replace the water in the reservoirs, improving the quality of these, reducing the eutrophication process. When rain they do not significantly occur or after long periods of shortages, the process of eutrophication and deterioration of water in dams increased significantly. Through knowledge of the behavior of the annual cycle of rainfall can have an intimate knowledge of how it may be the tendency of rainy or prone to shortages following period, mainly observing the trends of larger scale phenomena

A educação popular para todos de uma cidade educadora (Natal, Rio Grande do Norte, 1957-1964)

This work has as a research subject of popular education policies of the city of Natal, Rio Grande do Norte, in the years 1957 to 1964. It aims to identify and analyze popular education policies developed and implemented by the Municipality of Natal in these years. To get the historical data, we establish as a guiding reserch question the following: Which elaborated educational policies were implemented by the Municipality of Natal in the years 1957-1964? and took over as the method Evidential Paradigm as proposition in Pinheiro (2009). This is anchored in documentary sources of Educational Legislation at National, State and Municipal levels as well as in the newspapers Folha de Tarde and Jornal de Natal; in existing documents from the archives of the Historical and Geographical Institute of Rio Grande do Norte (IHGRN), the Municipal Public Archives of Natal; iconographic sources; interviews and academic publications. In addition to these sources, we were inspired by the works of Aristotles (2011), Hobbes (2009), Freire (2011), Góes (1980), Germano (1989), Cortez (2005) and Galvão (2004). This research allowed us to understand that policies of popular education of Natal (RN) were based on a democratic educational practice, supported on three pillars, namely: participation and involvement of Natal population; construction and reconstruction of teaching practices in prioritizing their action programs to mass literacy and the training of lay teachers; and the democratization of culture. This historical process made Natal on educating city.

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