13 resultados para Previsão da taxa de inflação

em Universidade Federal do Rio Grande do Norte(UFRN)


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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

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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

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

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Telecommunication is one of the most dynamic and strategic areas in the world. Many technological innovations has modified the way information is exchanged. Information and knowledge are now shared in networks. Broadband Internet is the new way of sharing contents and information. This dissertation deals with performance indicators related to maintenance services of telecommunications networks and uses models of multivariate regression to estimate churn, which is the loss of customers to other companies. In a competitive environment, telecommunications companies have devised strategies to minimize the loss of customers. Loosing customers presents a higher cost than obtaining new ones. Corporations have plenty of data stored in a diversity of databases. Usually the data are not explored properly. This work uses the Knowledge Discovery in Databases (KDD) to establish rules and new models to explain how churn, as a dependent variable, are related to a diversity of service indicators, such as time to deploy the service (in hours), time to repair (in hours), and so on. Extraction of meaningful knowledge is, in many cases, a challenge. Models were tested and statistically analyzed. The work also shows results that allows the analysis and identification of which quality services indicators influence the churn. Actions are also proposed to solve, at least in part, this problem

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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

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Epilepsies are neurological disorders characterized by recurrent and spontaneous seizures due to an abnormal electric activity in a brain network. The mesial temporal lobe epilepsy (MTLE) is the most prevalent type of epilepsy in adulthood, and it occurs frequently in association with hippocampal sclerosis. Unfortunately, not all patients benefit from pharmacological treatment (drug-resistant patients), and therefore become candidates for surgery, a procedure of high complexity and cost. Nowadays, the most common surgery is the anterior temporal lobectomy with selective amygdalohippocampectomy, a procedure standardized by anatomical markers. However, part of patients still present seizure after the procedure. Then, to increase the efficiency of this kind of procedure, it is fundamental to know the epileptic human brain in order to create new tools for auxiliary an individualized surgery procedure. The aim of this work was to identify and quantify the occurrence of epilepticform activity -such as interictal spikes (IS) and high frequency oscillations (HFO) - in electrocorticographic (ECoG) signals acutely recorded during the surgery procedure in drug-resistant patients with MTLE. The ECoG recording (32 channels at sample rate of 1 kHz) was performed in the surface of temporal lobe in three moments: without any cortical resection, after anterior temporal lobectomy and after amygdalohippocampectomy (mean duration of each record: 10 min; N = 17 patients; ethic approval #1038/03 in Research Ethic Committee of Federal University of São Paulo). The occurrence of IS and HFO was quantified automatically by MATLAB routines and validated manually. The events rate (number of events/channels) in each recording time was correlated with seizure control outcome. In 8 hours and 40 minutes of record, we identified 36,858 IS and 1.756 HFO. We observed that seizure-free outcome patients had more HFO rate before the resection than non-seizure free, however do not differentiate in relation of frequency, morphology and distribution of IS. The HFO rate in the first record was better than IS rate on prediction of seizure-free patients (IS: AUC = 57%, Sens = 70%, Spec = 71% vs HFO: AUC = 77%, Sens = 100%, Spec = 70%). We observed the same for the difference of the rate of pre and post-resection (IS: AUC = 54%, Sens = 60%, Spec = 71%; vs HFO: AUC = 84%, Sens = 100%, Spec = 80%). In this case, the algorithm identifies all seizure-free patients (N = 7) with two false positives. To conclude, we observed that the IS and HFO can be found in intra-operative ECoG record, despite the anesthesia and the short time of record. The possibility to classify the patients before any cortical resection suggest that ECoG can be important to decide the use of adjuvant pharmacological treatment or to change for tailored resection procedure. The mechanism responsible for this effect is still unknown, thus more studies are necessary to clarify the processes related to it

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Hebb postulated that memory could be stored thanks to the synchronous activity of many neurons, building a neural assembly. Knowing of the importance of the hippocampal structure to the formation of new explicit memories, we used electrophysiological recording of multiple neurons to access the relevance of rate coding from neural firing rates in comparison to the temporal coding of neural assemblies activity in the consolidation of an aversive memory in rats. Animals were trained at the discriminative avoidance task using a modified elevated plus-maze. During experimental sessions, slow wave sleep periods (SWS) were recorded. Our results show an increase in the identified neural assemblies activity during post-training SWS, but not for the neural firing rate. In summary, we demonstrate that for this particular task, the relevant information needed for a proper memory consolidation lies within the temporal patters of synchronized neural activity, not in its firing rate

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The ability to predict future rewards or threats is crucial for survival. Recent studies have addressed future event prediction by the hippocampus. Hippocampal neurons exhibit robust selectivity for spatial location. Thus, the activity of hippocampal neurons represents a cognitive map of space during navigation as well as during planning and recall. Spatial selectivity allows the hippocampus to be involved in the formation of spatial and episodic memories, including the sequential ordering of events. On the other hand, the discovery of reverberatory activity in multiple forebrain areas during slow wave and REM sleep underscored the role of sleep on the consolidation of recently acquired memory traces. To this date, there are no studies addressing whether neuronal activity in the hippocampus during sleep can predict regular environmental shifts. The aim of the present study was to investigate the activity of neuronal populations in the hippocampus during sleep sessions intercalated by spatial exploration periods, in which the location of reward changed in a predictable way. To this end, we performed the chronic implantation of 32-channel multielectrode arrays in the CA1 regions of the hippocampus in three male rats of the Wistar strain. In order to activate different neuronal subgroups at each cycle of the task, we exposed the animals to four spatial exploration sessions in a 4-arm elevated maze in which reward was delivered in a single arm per session. Reward location changed regularly at every session in a clockwise manner, traversing all the arms at the end of the daily recordings. Animals were recorded from 2-12 consecutive days. During spatial exploration of the 4-arm elevated maze, 67,5% of the recorded neurons showed firing rate differences across the maze arms. Furthermore, an average of 42% of the neurons showed increased correlation (R>0.3) between neuronal pairs in each arm. This allowed us to sort representative neuronal subgroups for each maze arm, and to analyze the activity of these subgroups across sleep sessions. We found that neuronal subgroups sorted by firing rate differences during spatial exploration sustained these differences across sleep sessions. This was not the case with neuronal subgroups sorted according to synchrony (correlation). In addition, the correlation levels between sleep sessions and waking patterns sampled in each arm were larger for the entire population of neurons than for the rate or synchrony subgroups. Neuronal activity during sleep of the entire neuronal population or subgroups did not show different correlations among the four arm mazes. On the other hand, we verified that neuronal activity during pre-exploration sleep sessions was significantly more similar to the activity patterns of the target arm than neuronal activity during pre-exploration sleep sessions. In other words, neuronal activity during sleep that precedes the task reflects more strongly the location of reward than neuronal activity during sleep that follows the task. Our results suggest that neuronal activity during sleep can predict regular environmental changes

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This study investigates the chemical species produced water from the reservoir areas of oil production in the field of Monte Alegre (onshore production) with a proposal of developing a model applied to the identification of the water produced in different zones or groups of zones.Starting from the concentrations of anions and cátions from water produced as input parameters in Linear Discriminate Analysis, it was possible to estimate and compare the model predictions respecting the particularities of their methods in order to ascertain which one would be most appropriate. The methods Resubstitution, Holdout Method and Lachenbruch were used for adjustment and general evaluation of the built models. Of the estimated models for Wells producing water for a single production area, the most suitable method was the "Holdout Method and had a hit rate of 90%. Discriminant functions (CV1, CV2 and CV3) estimated in this model were used to modeling new functions for samples ofartificial mixtures of produced water (producedin our laboratory) and samples of mixtures actualproduced water (water collected inwellsproducingmore thanonezone).The experiment with these mixtures was carried out according to a schedule experimental mixtures simplex type-centroid also was simulated in which the presence of water from steam injectionin these tanks fora part of amostras. Using graphs of two and three dimensions was possible to estimate the proportion of water in the production area

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Nowadays several electronics devices support digital videos. Some examples of these devices are cellphones, digital cameras, video cameras and digital televisions. However, raw videos present a huge amount of data, millions of bits, for their representation as the way they were captured. To store them in its primary form it would be necessary a huge amount of disk space and a huge bandwidth to allow the transmission of these data. The video compression becomes essential to make possible information storage and transmission. Motion Estimation is a technique used in the video coder that explores the temporal redundancy present in video sequences to reduce the amount of data necessary to represent the information. This work presents a hardware architecture of a motion estimation module for high resolution videos according to H.264/AVC standard. The H.264/AVC is the most advanced video coder standard, with several new features which allow it to achieve high compression rates. The architecture presented in this work was developed to provide a high data reuse. The data reuse schema adopted reduces the bandwidth required to execute motion estimation. The motion estimation is the task responsible for the largest share of the gains obtained with the H.264/AVC standard so this module is essential for final video coder performance. This work is included in Rede H.264 project which aims to develop Brazilian technology for Brazilian System of Digital Television

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Este trabalho aborda o problema de otimização em braquiterapia de alta taxa de dose no tratamento de pacientes com câncer, com vistas à definição do conjunto de tempos de parada. A técnica de solução adotada foi a Transgenética Computacional apoiada pelo método L-BFGS. O algoritmo desenvolvido foi empregado para gerar soluções não denominadas cujas distribuições de dose fossem capazes de eiminar o câncer e ao mesmo tempo preservar as regiões normais

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The research aimed two objectives: 1st) identifying and describing the metaphors of the inflation, in a corpus of 18 texts of economic journalism, from Joelmir Beting, written in the last trimester of 2002, at the moment of the government s transition of president Fernando Henrique Cardoso to Luiz Inácio Lula Da Silva. 2nd) verifying the recognition of the metaphors by the students of the basic education of a private school from Natal. 91 metaphors had been identified, analyzed in the perspective of the conceptual metaphor s theory, by Lakoff and Johnson (2002), on the basis of the distinction between conceptual metaphor and metaphoric expressions, and between domain-source/domain-target. 10 underlying conceptual metaphors had been inferred, being that the domains-source used more frequently to characterize the inflation had been those ones according to the human being and the animals and, of a less imaginable form, to the ways of transport (car, aircraft). These general conceptual metaphors had been unfolded in other s more specific ones ( animal specifying itself in lion , dragon , dog , etc.). Another result was the identification of metaphoric expressions with two or more meanings , with relation to more than one conceptual metaphor or explicit, in the same expression, two domains-source (for example: armored dragon ) and contributes, of a relevant form, for the semantic struturation of the text. The understanding of the metaphors was verified through an activity of domains-source s identification (10 metaphoric statements and fulfilling of the gap in the phrase the inflation is a/an ) applied in a group of 8th year of the basic education (12-13 years old, with 14 girls and 17 boys) from a school of good social and economic positions from Natal-RN. There weren t great difficulties on the part of the students in recognizing the domains-source involved: about 80% to the great majority of the statements

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The research aimed two objectives: 1st) identifying and describing the metaphors of the inflation, in a corpus of 18 texts of economic journalism, from Joelmir Beting, written in the last trimester of 2002, at the moment of the government s transition of president Fernando Henrique Cardoso to Luiz Inácio Lula Da Silva. 2nd) verifying the recognition of the metaphors by the students of the basic education of a private school from Natal. 91 metaphors had been identified, analyzed in the perspective of the conceptual metaphor s theory, by Lakoff and Johnson (2002), on the basis of the distinction between conceptual metaphor and metaphoric expressions, and between domain-source/domain-target. 10 underlying conceptual metaphors had been inferred, being that the domains-source used more frequently to characterize the inflation had been those ones according to the human being and the animals and, of a less imaginable form, to the ways of transport (car, aircraft). These general conceptual metaphors had been unfolded in other s more specific ones ( animal specifying itself in lion , dragon , dog , etc.). Another result was the identification of metaphoric expressions with two or more meanings , with relation to more than one conceptual metaphor or explicit, in the same expression, two domains-source (for example: armored dragon ) and contributes, of a relevant form, for the semantic struturation of the text. The understanding of the metaphors was verified through an activity of domains-source s identification (10 metaphoric statements and fulfilling of the gap in the phrase the inflation is a/an ) applied in a group of 8th year of the basic education (12-13 years old, with 14 girls and 17 boys) from a school of good social and economic positions from Natal-RN. There weren t great difficulties on the part of the students in recognizing the domains-source involved: about 80% to the great majority of the statements