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

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

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Uma fonte chaveada de 50kW com correção de fator de potência para alimentação de uma tocha de plasma Indutiva utilizando técnicas de controle digital

This work deals with the development of an experimental study on a power supply of high frequency that provides the toch plasmica to be implemented in PLASPETRO project, which consists of two static converters developed by using Insulated Gate Bipolar Transistor (IGBT). The drivers used to control these keys are triggered by Digital Signal Processor (DSP) through optical fibers to reduce problems with electromagnetic interference (EMI). The first stage consists of a pre-regulator in the form of an AC to DC converter with three-phase boost power factor correction which is the main theme of this work, while the second is the source of high frequency itself. A series-resonant inverter consists of four (4) cell inverters operating in a frequency around 115 kHz each one in soft switching mode, alternating itself to supply the load (plasma torch) an alternating current with a frequency of 450 kHz. The first stage has the function of providing the series-resonant inverter a DC voltage, with the value controlled from the power supply provided by the electrical system of the utility, and correct the power factor of the system as a whole. This level of DC bus voltage at the output of the first stage will be used to control the power transferred by the inverter to the load, and it may vary from 550 VDC to a maximum of 800 VDC. To control the voltage level of DC bus driver used a proportional integral (PI) controller and to achieve the unity power factor it was used two other proportional integral currents controllers. Computational simulations were performed to assist in sizing and forecasting performance. All the control and communications needed to stage supervisory were implemented on a DSP

Gestão de estoques: uma proposta de reposição contínua para material médico hospitalar e medicamentos em um hospital universitário federal do estado do Rio Grande do Norte

The inventory management in hospitals is of paramount importance, since the supply materials and drugs interruption can cause irreparable damage to human lives while excess inventories involves immobilization of capital. Hospitals should use techniques of inventory management to perform replenishment in shorter and shorter intervals, in order to reduce inventories and fixed assets and meet citizens requirements properly. The inventory management can be an even bigger problem for public hospitals, which have restrictions on the use of resources and decisionmaking structure more bureaucratized. Currently the University Hospital Onofre Lopes (HUOL) uses a periodic replacement policy for hospital medical supplies and medicines, which involves one moment surplus stock replenishment, the next out of stock items. This study aims to propose a system for continuous replenishment through order point for inventory of medical supplies and medicines to the hospital HUOL. Therefore, a literature review of Federal University Hospitals Management, Logistics, Inventory Management and Replenishment System in Hospitals was performed, emphasizing the demand forecast, classification or ABC curve and order point system. And also, policies of inventory management and the current proposal were described, dealing with profile of the mentioned institution, the current policy of inventory management and simulation for continuous replenishment order point. For the simulation, the sample consisted of 102 and 44 items of medical and hospital drugs, respectively, selected using the ABC classification of inventory, prioritizing items of Class A, which contains the most relevant items in added value, representing 80 % of the financial value in 2012 fiscal year. Considering that it is a public organization, subject to the laws, we performed two simulations: the first, following the signs for inventory management of Instruction No. 205 (IN 205 ), from Secretary of Public Administration of the Presidency ( SEDAP / PR ), and the second, based on the literature specializing in inventory management hospital. The results of two simulations were compared to the current policy of replenishment system. Among these results are: an indication that the system for continuous replenishment reorder point based on IN 205 provides lower levels of safety stock and maximum stock, enables a 17% reduction in the amount spent for the full replenishment of inventories, in other words, decreasing capital assets, as well as reduction in stock quantity, also the simulation made from the literature has indicated parameters that prevent the application of this technique to all items of the sample. Hence, a change in inventory management of HUOL, with the application of the continuous replenishment according to IN 205, provides a significant reduction in acquisition costs of medical and hospital medicine

Gestão por Competências em Organizações Públicas: O Processo de Transformação Organizacional e seus Impactos Junto aos Servidores na Secretaria Estadual de Administração e Recursos Humanos (SEARH/RN)

The state has changed over time in order to meet a society with increasingly stringent demands. Techniques of private means begin to be employed in an attempt to overcome the dysfunctions entrenched bureaucracy, making the machine faster. By federal law by the People Management Skills was established as a reference for the administration of Human Resources of the public sector in an attempt to develop professionally servers, based mainly on the three pillars of the model: the knowledge, skills and attitudes. This thesis aims at understanding, in the view of employees, the perceived impacts on the organizational changes occurring in the Department of Administration and Human Resources of the State of Rio Grande do Norte in order to implement a People Management Skills-based. It is a simple case study, characterized by the research during a certain period of time, collecting data in a real environment of an organization, in this case SEARH/RN. The procedures used in collecting data were the literature review, documental research and field research. We used a qualitative approach with exploratory and descriptive approach. Every reform was implemented in the institution and reported from there analyzed the impacts observed by the servers. As a result we observed a considerable advance in institutional activities, mainly relating to physical structure / organizational and human resource policies, with minor advances on labor policies, in much the result of the guiding focus of the reform on SEARH/RN. The impacts in total were more positive than negative and direct paths to improvement in public organizations. Making a general analysis of the modernization program implemented in SEARH/RN, we can conclude that there was a distinct change in all dimensions studied, mostly pointing out positive aspects, and contrary to the opinion of some authors, who claim to be very difficult to implement reforms in public organizations, since they are highly institutionalized environments. What was found was a big organization, with gaps and weaknesses, but with a much larger number of hits and recognition from institutional actors

Um Modelo híbrido para previsão de curvas de produção de petróleo

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior