Navegando por Assunto "Análise de séries temporais"

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Ascensão dos investidores pessoa física na bolsa de valores: uma análise descritiva do perfil do investidor e previsão utilizando a metodologia ARIMA
(2021-07-21) Simão Júnior, Jaime José; Carazza, Luís Eduardo Barbosa; http://lattes.cnpq.br/1625397086677760
This paper aims to study and understand the rise of new investors in the stock exchange, as well as to draw a detailed profile of the characteristics of this part of the population interested in income investments variable and perform a forecast using the Arima methodology. To achieve this objective, data was collected on minority investors directly from B3 - Brasil, Bolsa, Balcão, which is the company responsible for the stock exchange in Brazil. In this way, characteristics such as region, gender, age group were analyzed to draw a profile about the shareholders. In recent years, there has been a large increase in new investors that invest their resources in stocks and other related investments. Of Form, it's necessary to investigate whether such rise's structural and permanent or are only a consequence of A passing movement that will break down at some future time. A Forecasting methodology Arima will try to answer this question with a forecast for the first months of 2021, to identify whether this upward movement is maintained for those months. However, some Limitations in the forecast will be highlighted.
Criminalidade e desempenho econômico: uma análise em séries temporais para a Região Metropolitana do Recife
(2018) Silva Junior, Ivanildo Batista da; Silva, Diego Firmino Costa da; http://lattes.cnpq.br/8895265465747877; http://lattes.cnpq.br/7725162413337027
Métodos de previsão de consumo de energia elétrica residencial em grande volume de dados
(2019) Carvalho, Daniel José de; Medeiros, Victor Wanderley Costa de; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/6867315638833821
Electricity is one of the primary sources of energy used by humanity. Growing concern for the preservation of the environment has stimulated the search for renewable energy sources capable of reducing impacts on nature. Population growth and the increasingly frequent use of electronic devices in almost all daily activities demand the most efficient use of electricity. Due to these challenges, it is essential to carry out planning to dimen-sion the structure of generation and transmission of electric energy. One of the tools capable of assisting in this sizing is the demand forecasting. Another major challenge in this area lies in the realization of these forecasts in large data scenarios (Big Data). This work aims to evaluate the performance of two prediction methods, ARIMA andHolt-Winters, using temporal series applied to a large volume of data. The database was provided by the DEBS 2014 Grand Challenge event, which contains electricity consumption data for a large number of households for one month. For the application of the prediction methods, we used libraries in the R language. In order to process data,the Apache Spark framework was used in conjunction with the R language to parallelize the data reading processing and filtering parameters. The treated data were convertedin to time series with hourly consumption values, throughout the month comprised by theoriginal database. Predictions were made for the region of the households as a who leand each residence individually. The results showed an advantage of ARIMA versusHolt-Winters in the scenario used, using the RMSE metric as a comparative basis of performance. However, based on similar experiments found in the literature, with due proportions, both RMSE values are within an acceptable range.
Redes neurais artificiais aplicadas à previsão de surtos de leptospirose
(2018) Dantas, Elias Ferraz; Pires, Glauber Magalhães; http://lattes.cnpq.br/1159581534108735; http://lattes.cnpq.br/7510731560745063
Leptospirosis is a disease of global occurrence, especially in tropical regions with high rainfall incidence and in places with poor sanitary conditions. It is a disease considered neglected, underestimated and difficult to diagnose because it presents symptoms common to other diseases, besides having a high lethality rate when reaching serious levels. In Brazil, its treatment has a high hospital cost and the disease has a high incidence in large urban centers. In the state of Pernambuco, Recife, it stands out from the other cities for having significantly larger numbers of records of Leptospirosis infections, this worrying data extols the need to support epidemiological surveillance in the fight against the disease. The objective of this work is to analyze the feasibility of using Artificial Neural Networks applied to the forecast of Time Series to perform early identification of the number of cases of Leptospirosis in the city of Recife / Pernambuco in order to demonstrate the possibility of using the technique for forecasting outbreaks as an additional means of information to contribute to the planning of health prevention actions, minimizing the social and economic impact caused by the disease.
Suporte a decisão no setor sucroalcooleiro
(2019) Silva, João Vitor da; Gonçalves, Glauco Estácio; http://lattes.cnpq.br/6157118581200722
The sugar and alcohol sector is one of the largest agricultural sectors in Brazil. Each harvest millions of liters of ethanol and thousands of tons of sugar are imported worldwide.Despite the size of the sector, there are several problems that haunt the sugarcane producer. One is the drop in production causing sugar and ethanol production stops.This paper aims to carry out a comparative study of time series forecasting methodsin historical sugarcane production data, together with the construction of operation al indicators to aid in decision making. The database was taken from the quarterly results published by São Martinho for its investors. São Martinho is a publicly traded companyand one of the largest sugar, alcohol and energy production plants in Brazil. The R language was used to carry out the study. The experiments of this work used the predictive model SARIMA, for its almost unanimity in the forecast of agricultural yields.RMSE, ME, and MAE. In the development of the operational indicators, the waste distribution function of the SARIMA model defined along with the forecasts of the modelitself was used.At the end of all the work, the best SARIMA model was obtained for the quarterly sugarcane production data together with the indicators of fall in production: probability off all in production by 30 % and probability of fall in production below quarterly average production.