Holt-Winters Forecasting for Brazilian Natural Gas Production

Nowadays, the market for natural gas production and its use as a source of energy supply has been growing substantially in Brazil. However, the use of tools that assist the industry in the management of production can be essential for the strategic decision-making process. In this intuit, this work aims to evaluate the formulation of Holt Winter\u27s additive and multiplicative time series to forecast Brazilian natural gas production. A comparison between the models and their forecast play a vital role for policymakers in the strategic plan, and the models estimated production values ​​for the year 2018 based on the information contained in the interval between 2010 and 2017. Therefore, It was verified that the multiplicative method had a good performance so that we can conclude this formulation is ideal for such an application since all the predicted results by this model showed greater accuracy within the 95% confidence interval

Forecasting incidence of tuberculosis cases in Brazil based on various univariate time-series models

Tuberculosis (TB) remains the world\u27s deadliest infectious disease and is a serious public health problem. Control for this disease still presents several difficulties, requiring strategies for the execution of immediate combat and intervention actions. Given that changes through the decision-making process are guided by current information and future prognoses, it is critical that a country\u27s public health managers rely on accurate predictions that can detect the evolving incidence phenomena. of TB. Thus, this study aims to analyze the accuracy of predictions of three univariate models based on time series of diagnosed TB cases in Brazil, from January 2001 to June 2018, in order to establish which model presents better performance. For the second half of 2018. From this, data were collected from the Department of Informatics of the Unified Health System (DATASUS), which were submitted to the methods of Simple Exponential Smoothing (SES), Holt-Winters Exponential Smoothing (HWES) and the Integrated Autoregressive Moving Average (ARIMA) model. In the performance analysis and model selection, six criteria based on precision errors were established: Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percent Error (MAPE) and Theil\u27s U statistic (U1 and U2). According to the results obtained, the HWES (0.2, 0.1, 0.1) presented a high performance in relation to the error metrics, consisting of the best model compared to the other two methodologies compared here

Estudo inicial sobre a evolução do novo CORONAVÍRUS (SARS-COV-2) no estado do Pará (Brasil), no período entre 17/03/2020 e 06/04/2020.

O presente artigo apresenta o estudo inicial sobre a evolução do novo coronavírus (SARS-CoV-2) no estado do Pará, desde a confirmação do primeiro infectado no dia 18/03/2020 até o dia 06/04/2020.O estudo apresenta também um modelo matemático para estimar o número de infectados até o dia 06/05/2020. Os resultados mostram que o modelo é confiável para predições de curto prazo, cuja evolução pode ser de 1 infectado em 18/03/2020 a 761 infectados em 18/04/2020.This paper presents the initial study on the evolution of Coronavirus (SARS-CoV-2) in the state of Pará, from the confirmation of the first infected on 18/03/2020 until 06/04/2020. The study also presents a mathematical model for estimating the number of infected by 06/05/2020. The results show that the model is reliable for short-term predictions, whose evolution can be from 01 infected on 03/18/2020 to 761 infected on 18/04/2020