The purpose of this article is to evaluate the application of forecasting models along with the use of residual control charts to assess production processes whose samples have autocorrelation characteristics. The main objective is to determine the efficiency of control charts for individual observations (CCIO) and exponentially weighted moving average (EWMA) charts when they are applied to residuals of models of AR(1) or MA(1) to detect outlier in autocorrelated processes. Considering autocorrelation strength and sign in the data series and the outlier range, the series were simulated accomplishing 640,000 sets. The series were contaminated by anomalous observations at 100th position, an AR(1) or MA(1) model were fitted, and the residuals were evaluated by CCIO and EWMA control charts; the points correctly detected as an autocorrelation were recorded. For the parameters investigated (autocorrelation and outlier range), a detection rate was generated in each chart, and nonparametric comparison tests were applied. The result of the tests showed the superiority (p < 0.05) of the CCIO chart for both models. The study of the influence of the sign and magnitude of the autocorrelation parameter showed no significant (p > 0.05) for either AR(1) or MA(1) charts and models. In this context, control charts for individual observations (CCIO) were confirmed to effectively detect outliers through residuals in industrial autocorrelated processes originated in first-order AR and MA models.
Renewable sources are responsible for more than half of Brazilian electric generation, which basically correspond to hydropower, biomass and wind sources. This research aimed to verify if the Autoregressive Integrated Moving Average (ARIMA) models present good performance in predicting electricity generation from biomass, hydropower and wind power for the first months of COVID-19 pandemic in Brazil. The best forecasting models adjusted for biomass, hydropower and wind generation was the SARIMA, since this model was able to identify seasonal effects of climatic instability, such as periods of drought. Based on the seasonality of the largest generating sources, renewable generation needs to be offset by other sources, as non-renewable, and more efforts are needed to make Brazilian electric matrix more sustainable.
A produção e a exportação de celulose são componentes importantes da economia no país. O objetivo desta pesquisa foi prognosticar o preço nos mercados interno e externo da celulose brasileira e avaliar a interferência entre o preço médio da celulose vendida em atacado e o preço médio da celulose exportada pelo Brasil. O estudo utilizou dados oriundos do Informativo Florestal do Centro de Estudos Avançados em Economia Aplicada (CEPEA), coletados entre junho de 2008 a março de 2018. Os modelos Autorregressivos Integrados de Médias Móveis (ARIMA) foram utilizados para prognosticar o preço da celulose em atacado e exportada pelo Brasil e, para analisar a inter-relação dessas variáveis, foi aplicado modelo Vetor Autorregressivo (VAR). Observou-se que o preço da celulose em atacado e da celulose exportada sofrem oscilações em períodos semelhantes, devido à relação direta com a cotação do dólar e com as crises financeiras nos países importadores. O modelo de melhor acurácia para prognosticar o preço da celulose em atacado foi o modelo ARIMA (1,1,0) enquanto o modelo ARFIMAX (1, d*,0) obteve o melhor desempenho para prognosticar o preço da celulose exportada. A partir da modelagem VAR, verificou-se a existência de inter-relações entre as variáveis, as quais transpareceram o forte impacto do preço da celulose em atacado sobre o preço da celulose exportada pelo Brasil. Assim, as metodologias empregadas foram eficazes para prognosticar e analisar as inter-relações entre as variáveis.
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