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

Desidratação de polpas de frutas pelo método foam-mat

This study aimed to investigate the foam mat drying process of pineapple and mango pulp, as well as to evaluate the final product quality. Initially, the selection of fruit and additives was conducted based on density and stability determinations of mango, seriguela, umbu and pineapple foams. After selecting pineapple and mango for further studies, the fruit pulps and fruit foams were characterized in regard to their physicochemical composition. The temperature (60oC or 70oC) and the foam thickness (4 and 11 mm) were evaluated in accordance to the obtained drying curves and after model adjustment. Mango and pineapple powders obtained at the best process conditions were characterized in regard to their physicochemical composition, solubility, reconstitution time. Yoghurts were prepared with the addition of pineapple and mango powders and they were evaluated for their sensory acceptance. Results show that the best drying rates were achieved by using 70o C and layers 4mm thick for both fruits. The Page model successfully fitted the drying experimental data and it can be used as a predictive model. Pineapple and mango powders showed acid pH, high soluble solids content, low water activity (approx. 0.25), lipids between 1.46% and 2.03%, protein around 2.00%, and ascorbic acid content of 17,73 mg/100g and 14.32 mg/100g, for mango and pineapple, respectively. It was observed higher ascorbic acid retention for pineapple and mango powders processed at 70o C, which would be explained by the lower drying time applied. The fruit powders exhibited high solubility and fast reconstitution in water. The sensory acceptance indexes for yoghurts with the addition of both fruit powders were higher than 70%, which reflect the satisfactory product acceptance

Estudo do equilíbrio líquido-vapor do sistema água+etanol+líquido iônico visando a separação do álcool anidro

Anhydrous ethanol is used in chemical, pharmaceutical and fuel industries. However, current processes for obtaining it involve high cost, high energy demand and use of toxic and pollutant solvents. This problem occurs due to the formation of an azeotropic mixture of ethanol + water, which does not allow the complete separation by conventional methods such as simple distillation. As an alternative to currently used processes, this study proposes the use of ionic liquids as solvents in extractive distillation. These are organic salts which are liquids at low temperatures (under 373,15 K). They exhibit characteristics such as low volatility (almost zero/ low vapor ), thermal stability and low corrosiveness, which make them interesting for applications such as catalysts and as entrainers. In this work, experimental data for the vapor pressure of pure ethanol and water in the pressure range of 20 to 101 kPa were obtained as well as for vapor-liquid equilibrium (VLE) of the system ethanol + water at atmospheric pressure; and equilibrium data of ethanol + water + 2-HDEAA (2- hydroxydiethanolamine acetate) at strategic points in the diagram. The device used for these experiments was the Fischer ebulliometer, together with density measurements to determine phase compositions. The experimental data were consistent with literature data and presented thermodynamic consistency, thus the methodology was properly validated. The results were favorable, with the increase of ethanol concentration in the vapor phase, but the increase was not shown to be pronounced. The predictive model COSMO-SAC (COnductor-like Screening MOdels Segment Activity Coefficient) proposed by Lin & Sandler (2002) was studied for calculations to predict vapor-liquid equilibrium of systems ethanol + water + ionic liquids at atmospheric pressure. This is an alternative for predicting phase equilibrium, especially for substances of recent interest, such as ionic liquids. This is so because no experimental data nor any parameters of functional groups (as in the UNIFAC method) are needed

Síndrome da fragilidade e fatores associados em idosos comunitários do município de Santa Cruz RN

The frailty in the elderly is characterized by decreased physiological reserves, and is associated with increased risk of disability and high vulnerability to morbidity and mortality. This study is part of a multicentric project on Frailty in Elderly Brazilians (REDE FIBRA). Aims: to investigate characteristics, prevalence and associated factors related to frailty. Metodology: We interviewed 391 elderly patients aged 65 years, selected randomly. Data collection was performed using a multidimensional questionnaire containing information about socio-demographic and clinical variables. To characterize the frail elderly, was considered the phenotype proposed by Fried. Data were analyzed using descriptive statistics, bivariate analysis (χ ²) and binary logistic regression. Results: The prevalence of frailty was 17.1%. In the final model of multivariate analysis, was obtained as factors associated with frailty: advanced chronological age (p <0.001), have comorbidity (p <0.035), show dependence on performing basic activities of daily living (p <0.010) and instrumental (p <0.003) and have poor perception of health status (p <0030). Conclusions: The factors associated with frailty suggest a predictive model helping to understand the syndrome, guiding actions that minimize adverse effects on the aging process

Distribuição e uso de habitat por peixes recifais em um gradiente ambiental: estudo de caso em recifes areníticos

There are several abiotic factors reported in the literature as regulators of the distribution of fish species in marine environments. Among them stand out structural complexity of habitat, benthic composition, depth and distance from the coast are usually reported as positive influencers in the diversity of difentes species, including reef fish. These are dominant elements in reef systems and considered high ecological and socioeconomic importance. Understanding how the above factors influence the distribution and habitat use of reef fish communities are important for their management and conservation. Thus, this study aims to evaluate the influence of these variables on the community of reef fishes along an environmental gradient of depth and distance from shore base in sandstone reefs in the coast of state of Rio Grande do Norte, Brazil. These variables are also used for creating a simple predictive model reef fish biomass for the environment studied. Data collection was performed through visual surveys in situ, and recorded environmental data (structural complexity of habitat, type of coverage of the substrate, benthic invertebrates) and ecological (wealth, abundance and reef fish size classes). As a complement, information on the diet were raised through literature and the biomass was estimated from the length-weight relationship of each species. Overall, the reefs showed a low coverage by corals and the Shallow reefs, Intermediate I and II dominated by algae and the Funds by algae and sponges. The complexity has increased along the gradient and positively influenced the species richness and abundance. Both attributes influenced in the structure of the reef fish community, increasing the richness, abundance and biomass of fish as well as differentiating the trophic structure of the community along the depth gradient and distance from the coast. Distribution and use of habitat by recifas fish was associated with food availability. The predictor model identified depth, roughness and coverage for foliose algae, calcareous algae and soft corals as the most significant variables influencing in the biomass of reef fish. In short, the description and understanding of these patterns are important steps to elucidate the ecological processes. In this sense, our approach provides a new understanding of the structure of the reef fish community of Rio Grande do Norte, allowing understand a part of a whole and assist future monitoring actions, evaluation, management and conservation of these and other reefs of 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