Objetivo: analisar o padrão temporal e estimar as taxas de mortalidade nas primeiras 24 horas de vida e por causas evitáveis no estado de Pernambuco no período de 2000 a 2021. Método: estudo ecológico, tendo como unidade de análise o trimestre. A fonte de dados foi constituída pelo Sistema de Informações sobre Mortalidade e pelo Sistema de Informações sobre Nascidos Vivos. A modelagem da série temporal foi conduzida segundo o Modelo Autorregressivo Integrado de Médias Móveis. Resultados: foram registrados 14.462 óbitos nas primeiras 24 horas de vida, sendo 11.110 (76,8%) evitáveis. Observa-se para os forecasts que a taxa de mortalidade nas primeiras 24 horas de vida variou de 3,3 a 2,4 por 1.000 nascidos vivos, e a taxa de mortalidade por causas evitáveis variou de 2,3 a 1,8 por 1.000 nascidos vivos. Conclusão: a previsão sugeriu avanços na redução da mortalidade nas primeiras 24 horas de vida no estado e por causas evitáveis. Os modelos ARIMA apresentaram estimativas satisfatórias para as taxas de mortalidade e por causas evitáveis nas primeiras 24 horas de vida. Objetivo 2 200 2021 Método ecológico trimestre Vivos Móveis Resultados 14462 14 462 14.46 11110 11 110 11.11 76,8% 768 76 8 (76,8% Observase Observa se 33 3 3, 4 2, 1000 1 000 1.00 vivos 23 18 1, Conclusão 20 202 1446 46 14.4 1111 11.1 76,8 7 (76,8 100 00 1.0 144 14. 111 11. 76, (76, 10 0 1. (76 (7 (

Objective: to analyze the temporal pattern and estimate mortality rates in the first 24 hours of life and from preventable causes in the state of Pernambuco from 2000 to 2021. Method: an ecological study, using the quarter as the unit of analysis. The data source was made up of the Mortality Information System and the Live Birth Information System. The time series modeling was conducted according to the Autoregressive Integrated Moving Average Model. Results: 14,462 deaths were recorded in the first 24 hours of life, 11,110 (76.8%) of which being preventable. It is observed from the forecasts that the mortality rate in the first 24 hours of life ranged from 3.3 to 2.4 per 1,000 live births, and the mortality rate from preventable causes ranged from 2.3 to 1.8 per 1,000 live births. Conclusion: the prediction suggested progress in reducing mortality in the first 24 hours of life in the state and from preventable causes. The ARIMA models presented satisfactory estimates for mortality rates and preventable causes in the first 24 hours of life. Objective 2 200 2021 Method study analysis Model Results 14462 14 462 14,46 11110 11 110 11,11 76.8% 768 76 8 (76.8% 33 3 3. 4 2. 1000 1 000 1,00 births 23 18 1. Conclusion 20 202 1446 46 14,4 1111 11,1 76.8 7 (76.8 100 00 1,0 144 14, 111 11, 76. (76. 10 0 1, (76 (7 (

Objetivo: analizar el patrón temporal y estimar las tasas de mortalidad en las primeras 24 horas de vida y por causas evitables en el estado de Pernambuco en el período de 2000 a 2021. Método: estudio ecológico, teniendo como unidad de análisis el trimestre. La fuente de datos se constituyó por el Sistema de Informaciones sobre Mortalidad y el Sistema de Informaciones sobre Nacidos Vivos. El modelado de series temporales se realizó según el Modelo Autorregresivo Integrado de Promedio Móvil. Resultados: se registraron 14.462 óbitos en las primeras 24 horas de vida, siendo 11.110 (el 76,8%) evitables. Se observa para los pronósticos ( forecasts) que la tasa de mortalidad en las primeras 24 horas de vida registro una variación de 3,3 a 2,4 por 1.000 nacidos vivos, y la tasa de mortalidad por causas evitables de 2,3 a 1,8 por 1.000 nacidos vivos. Conclusión: la predicción sugirió avances en la reducción de la mortalidad en las primeras 24 horas de vida en el estado y por causas evitables. Los modelos ARIMA presentaron estimaciones satisfactorias para las tasas de mortalidad y por causas evitables en las primeras 24 horas de vida. Objetivo 2 200 2021 Método ecológico trimestre Vivos Móvil Resultados 14462 14 462 14.46 11110 11 110 11.11 76,8% 768 76 8 forecasts 33 3 3, 4 2, 1000 1 000 1.00 vivos 23 18 1, Conclusión 20 202 1446 46 14.4 1111 11.1 76,8 7 100 00 1.0 144 14. 111 11. 76, 10 0 1.

Abstract The Internet is a growing source of knowledge and can provide information about oral health. This ecological study aimed to characterize the interests in toothpaste among Google users from different countries. Our hypothesis was that there would be an increase in Google users’ interest in information about toothpaste. This retrospective longitudinal ecological study analyzed the toothpaste-related interest of Google users from 10 countries between January 2004 and December 2020. The monthly variation in relative search volume (RSV) and the main related queries were determined using Google Trends. Autoregressive integrated moving average (ARIMA) forecasting models were built to establish the predictive RSV values for toothpaste for an additional 12 months. Autocorrelation plots and the generalized additive model (GAM) were used to diagnose trends and seasonality in RSV curves. Additionally, the influence of social isolation related to the outbreak of COVID-19 was analyzed. Although not detected by autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses, the heuristic analysis showed an increase in the interest in toothpaste-related information in all countries, with a stable trend observed in the 12-month forecasts, except for the increases in the United Kingdom and South Africa. Also, GAM analyses demonstrated a non-significant monthly or quarterly seasonal influence on data. In addition, social isolation during the COVID-19 pandemic did not influence the online information-seeking behavior of Google Search users linked to this topic. We confirmed the hypothesis that the interest of Google Search users in information about toothpaste increased in all of the 10 assessed countries. health toothpasterelated 1 200 2020 (RSV Trends ARIMA (ARIMA months (GAM curves Additionally COVID19 COVID 19 COVID-1 ACF (ACF PACF (PACF 12month month forecasts Africa Also nonsignificant non significant data addition informationseeking seeking topic 20 202 COVID1 COVID- 2

Abstract Corn is one of the most important products in the world due to its nutritional qualities related to human, animal consumption and industrial use. A predictor of corn price behavior is useful for producers and marketers in making decisions. A price indicator is provided in international stock exchanges; however, in Mexico there is no stock exchange that provides an adequate signal about the future behavior of white corn prices in Mexico. In this research, an analysis of white corn prices in Michoacán and Jalisco was carried out, using autoregressive integrated moving average (ARIMA) models with the aim of providing a predictor of white corn prices. Two models were built for each series and point estimates were made. The predictive capacity of the models was evaluated using the mean absolute percentage error, the root-mean-square error and Theil’s U. The results showed that the price of corn in Michoacán and Jalisco can be predicted by its past values with an AR (1) model and an MA (2) model. It was concluded that these models provide a predictor for corn prices and constitute a useful tool in planning and making decisions regarding the process of production, commercialization and related products.

Resumen El maíz es uno de los productos más importantes en el mundo debido a sus cualidades alimenticias relacionadas con el consumo humano, animal y uso industrial. Un predictor del comportamiento del precio del maíz es de utilidad para los productores y comercializadores en la toma de decisiones. Un indicador del precio es proporcionado en las bolsas internacionales; sin embargo, en México no existe una bolsa que proporcione una señal adecuada sobre el comportamiento futuro de los precios del maíz blanco en México. En esta investigación se realizó un análisis de los precios del maíz blanco en Michoacán y Jalisco, usando modelos autorregresivos integrados de media móvil (ARIMA) con el objetivo de proporcionar un predictor de los precios del maíz blanco. Se construyeron dos modelos para cada serie y se realizaron estimaciones puntuales. Se evaluó la capacidad predictiva de los modelos usando el error porcentual absoluto medio del inglés mean absolute percentage error, la raíz del error cuadrático medio y la U de Theil. Los resultados mostraron que el precio del maíz en Michoacán y Jalisco puede predecirse mediante sus valores pasados con un modelo AR (1) y un modelo MA (2). Se concluyó que estos modelos proporcionan un predictor para los precios de maíz y constituyen una herramienta útil en la planeación y toma de decisiones referentes al proceso productivo, de comercialización y productos relacionados.

Resumo As fontes renováveis são responsáveis por mais da metade da geração elétrica brasileira, as quais correspondem basicamente às fontes hidráulica, biomassa e eólica. A presente pesquisa teve como objetivo verificar se os modelos Autorregressivos Integrados de Médias Móveis (ARIMA) possuem bom desempenho ao prever a geração de eletricidade das fontes biomassa, hidráulica e eólica nos primeiros meses da pandemia da COVID-19 no Brasil. O melhor modelo de previsão ajustado para as fontes biomassa, hidráulica e eólica foi o SARIMA, uma vez que esse modelo foi capaz de identificar os efeitos sazonais causados por instabilidades climáticas, como períodos de estiagem. Devido à sazonalidade das principais fontes geradoras, a geração renovável precisa ser compensada com outras fontes, como as não renováveis. Dessa forma, mais esforços são necessários para tornar a matriz elétrica brasileira mais sustentável.

Abstract 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.

ABSTRACT Introduction: The mathematical models are abstractions of the reality that allow for the description of important characteristics of the phenomena studied. Among these, the epidemic models are considered one of the most powerful tools to analyze and understand epidemic propagation and control. Objective: A model for the forecast of spread scenarios of the COVID-19 has been developed. Methods: A proposed population model inspired by analogy by hydrologic routing in rivers, an autoregressive integrated moving average model (ARIMA) and a transition model were combined in order to evaluate three possible spread scenarios and their temporary evolution during the development of COVID-19 pandemic. Results: In this contribution, 27 countries having coronavirus daily reports until April 16th, 2020 were studied. They were grouped by quartiles according to rates of recovered and deceased people. This information permitted the establishment of criteria for the parameters involved in the proposed model for Cuba as a case study. The favorable, not very favorable and critical epidemic spread scenarios were generated from ARIMA up to April 29th, 2020 and the transition model extended to 120 days. The results of the model implementation were compared with the real data of the pandemic evolution in Cuba up to May 22nd, 2020, resulting in a satisfactory correspondence between the real daily reports and the scenarios of active cases. This model could be used in the same way for other American countries that also have a delay with regard to the countries selected for this study.

RESUMEN Introducción: Los modelos matemáticos son abstracciones de la realidad que permiten describir características importantes de los fenómenos en estudio. Entre estos los modelos epidemiológicos son considerados una de las herramientas más poderosas para analizar y comprender la propagación y el control de epidemias. Objetivos: desarrollar un modelo para el pronóstico de escenarios de contagio de la COVID-19. Métodos: Se combinaron el modelo propuesto de población inspirado en el tránsito hidrológico en ríos, un modelo autorregresivo de medias móviles (ARIMA) y un modelo de transición para evaluar tres escenarios posibles de contagio y su evolución temporal durante el desarrollo de la pandemia de la COVID-19. Resultados: Fueron estudiados 27 países con reporte de contagio hasta el 16 de abril de 2020 y fueron agrupados por cuartiles según un parámetro que relaciona personas recuperadas y fallecidas. Esta información permitió establecer criterios sobre los parámetros involucrados en el modelo propuesto para el caso de Cuba. Los escenarios de contagio de la epidemia favorable, poco favorable y crítico fueron generados a partir de modelos ARIMA hasta el 29 de abril de 2020 y de transición, extendido a 120 días. Los resultados de la implementación del modelo fueron comparados con los datos reales de la evolución de la pandemia en Cuba hasta el 22 de mayo, obteniéndose una correspondencia satisfactoria entre los registros reales y los escenarios de casos activos. Este modelo podría utilizarse de la misma forma para otros países de América que tienen también un retardo con respecto a los países seleccionados para este estudio.

Abstract: This work presents a comparison between two algorithms for the prediction of glucose levels in diabetic patients by using a univariate time series. The algorithms are applied to the history of fasting glucose levels to predict the five following values. The comparison is performed between 1) The Autoregressive Neural Networks (ARNN) and 2) The autoregressive integrated moving average (ARIMA) models. A total of 70 series are analyzed, and we show that the results obtained for the ARIMA model have error percentages higher than 25% of the predicted value to the expected value. In contrast, in 73% of the cases, the percentage error was less than 25% for the Autoregressive Neural Networks.

Resumen: Este trabajo presenta una comparación entre dos algoritmos para la predicción de los niveles de glucosa en pacientes diabéticos mediante el uso de series de tiempo univariadas. Los algoritmos se aplican al historial de niveles de glucosa en ayunas para predecir los 5 valores posteriores. La comparación se realiza entre 1) Las Redes Neuronales Autorregresivas (ARNN) y 2) Los modelos de media móvil integrada autorregresiva (ARIMA), se analizan un total de 70 series, y se muestra que los resultados obtenidos para el modelo ARIMA tienen porcentajes de error mayores del 25% del valor predicho con respecto al valor esperado, mientras que para las Redes Neuronales Autorregresivas en el 73% de los casos el porcentaje de error fue menor al 25%.

Resumo 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.

Abstract The production and export of cellulose are important components of the Brazilian economy. The aim of this research was to predict the domestic and foreign prices of the Brazilian cellulose and to evaluate the interference between its average price sold at wholesale and exported by Brazil. The study focused on data from the Forestry Newsletter of the Center for Advanced Studies on Applied Economics (CEPEA), collected from June 2008 to March 2018. The Autoregressive Integrated Moving Average (ARIMA) models were used to predict the wholesale and export price of cellulose, while the Vector Autoregressive (VAR) model was applied to analyze the inter-relation of these variables. It was observed that the price of wholesale and exported celluloses vary in similar periods, due to the direct relationship with the dollar quotation and with the financial crises in the importing countries. The most accurate model adjusted to predict the wholesale cellulose price was the ARIMA model (1,1,0), while ARFIMAX model (1, d*, 0) obtained the best performance to predict exported cellulose price. Based on the VAR model, there was a correlation between variables, which means that wholesale cellulose price has a strong impact on exported cellulose price. Thus, the methodologies used were effective to predict and analyze the inter-relationships between the variables.

Resumo Na América do Sul, a fasciolose causada pelo Trematoda Fasciola hepatica é uma antropozoonose associada a perdas econômicas significativas e baixo grau de bem-estar animal. O objetivo deste estudo foi determinar a prevalência de F. hepatica no fígado de búfalos abatidos entre 2003 a 2017 e realizar uma análise de previsão da doença para os próximos cinco anos, utilizando o modelo Auto-Regressivo Integrado de Médias Móveis (ARIMA). A análise dos dados revelou uma incidência total de 7.187 casos em 226.561 indivíduos. Houve um acentuado grau de variação interanual nas taxas de prevalência (p<0,005). Fasciola hepatica foi mais prevalente nos estados do sul do Brasil; Paraná, Rio Grande do Sul e Santa Catarina, com 11,9; 7,7; e 3,2% de fígados condenados, respectivamente. A alta incidência de condenação de fígado no Paraná foi influenciada pelo fator climático. Os modelos ARIMA indicaram uma tendência constante na ocorrência da doença, destacando um padrão futuro da doença. Os modelos também descreveram cenários de piora e de possível melhoria, calculando os efeitos de medidas de intervenção. Assim, existe a urgência de realizar diagnóstico constante nos animais (coprológico e diagnóstico imunológico) e no ambiente, para que se evite os altos índices de infecção.

Abstract In South America, fascioliasis caused by the trematode Fasciola hepatica is an anthropozoonosis disease associated with significant economic losses and poor animal welfare. The objective of this study was to determine the prevalence of F. hepatica in the liver of buffaloes slaughtered from 2003 to 2017 in Brazil, and to perform a forecast analysis of the disease for the next five years using the Autoregressive Integrated Moving Average (ARIMA) model. Data analysis revealed an incidence of 7,187 cases out of 226,561 individuals. The disease presented a considerable interannual variation (p<0.005). Fasciola hepatica was more prevalent in the southern states of Brazil; Paraná, Rio Grande do Sul, and Santa Catarina, presenting 11.9, 7.7, and 3.2% of infected livers, respectively. The high frequency of liver condemnation in Paraná was influenced by weather conditions. The ARIMA models calculated a constant trend of the disease, depicting an average of its future prevalence. The models also described a worse-case and a positive-case scenario, calculating the effects of intervention measurements. In reality, there is an urgent need for regular diagnostic in the animals (fecal and immune diagnose) and in the environment (intermediate host), in order to avoid the high rates of infection.

INTRODUCTION Dengue is a disease caused by any one of five virus serotypes and transmitted to humans by the Aedes aegypti mosquito. Climate change and health conditions have combined to make dengue a global public health problem. The situation is especially serious in Colombia, where by week 36 of 2018, dengue incidence was 96 cases per 100,000 population, with a total of 111 deaths. Different mathematical and statistical models have been proposed to understand the dynamics of transmission and consequently to apply control strategies to reduce the number of dengue cases. OBJECTIVE Forecast the number of dengue cases expected in Colombia from 2018 through 2022 with the stochastic Auto-Regressive Integrated Moving Average (ARIMA) model and use the results to adjust the parameters of an ordinary differential equations model in order to determine the disease’s basic reproduction number in the year presenting the highest number of dengue cases. METHODS An ecological time series study was conducted to forecast dengue incidence in Colombia from 2018 through 2022. The data were compiled from Colombia’s National Health Institute series on dengue cases reported by epidemiological week from 2009 to 2017. The stochastic ARIMA time series model was applied. Forecasts were then analyzed, and the year with the highest number of predicted cases was used to adjust the parameters of an ordinary differential equations model (ODE) through nonlinear least squares regression to calculate the vectorial capacity of the transmitting mosquito. RESULTS Forecasts of the total number of dengue cases per year in Colombia for the following five years were: 32,411 (2018); 88,221 (2019); 56,392 (2020); 47,940 (2021); and 77,344 (2022). The highest number of cases was forecast for 2019. Values for the parameters affecting dengue transmission that year (by the year’s four quarters), such as recovery rate (0.0992, 0.0838, 0.1177, and 0.1535, respectively), vectorial capacity of the transmitting mosquito (0.1720, 0.1705, 0.1204, and 0.2147, respectively) and the basic dengue reproduction number (1.73, 2.03, 1.02, and 1.40, respectively) were estimated, indicating that most cases would occur in the second quarter and, since the basic reproduction number values were >1, the disease would persist in the country throughout the entire year. CONCLUSIONS ARIMA model forecasts for 2018 through 2022 predicted the highest incidence of dengue cases in Colombia would occur in 2019. Comparison of ARIMA model forecasts and the ODE model allowed projections of possible variations in dengue cases reported, and the basic reproduction number predicted that dengue would persist throughout 2019.

This paper proposes and discusses Autoregressive Moving Average (ARMA), Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) time series models for broadband power line communication (PLC) networks with impulsive noise enviroment in the frequency range of 1 - 30 MHz. In time series modelling and analysis, time series models are fitted to the acquired time series describing the system for purposes which include simulation, forecasting, trend assessment, and a better understanding of the dynamics of the impulsive noise in PLC systems. Also, because the acquired impulsive noise measurement data are observations made over time, time series models constitute important statistical tools for use in solving the problem of impulsive noise modelling and forecasting in PLC. In fact, the time series and other statistical methods presented in numerous available literature draw upon research developments from two areas of environmetrics called stochastic hydrology and statistical water quality modelling as well as research contributions from the field of statistics. In time series modelling and analysis, we determine the most appropriate stochastic or time series model to fit our acquired data set at the confirmatory data analysis stage. No matter what type of stochastic model is to be fitted to the data set, we follow the identification, estimation, and diagnostic check stages of model construction. In addition, we explore the resulting autocorrelation functions in estimating the parameters of the selected time series models. Finally, SARIMA model is found suitable for computer-based PLC systems simulations and forecasting based on the diagnostic checks.

Resumo Esse trabalho tem como objetivo realizar previsões de séries temporais da velocidade do vento em termos de médias mensais no nordeste brasileiro. Foram testados os seguintes modelos de séries temporais, Auto-Regressivo Integrado de Médias Móveis (ARIMA) e Holt–Winters (HW), e também inteligência artificial computacional com o uso de Redes Neurais Artificiais (RNA). E ainda foram testados dois modelos híbridos, o primeiro com combinação dos modelos ARIMA e RNA, o qual existem trabalhos na literatura, e o segundo trata-se de uma tentativa de combinação dos modelos HW e RNA. As séries ajustadas obtidas pelos modelos híbridos, mostram-se eficientes em acompanhar o perfil das séries observadas das regiões de estudo, com semelhanças aos dados observados em termos de máximos e mínimos, indicando assim, a capacidade dos modelos em representar sazonalidades. O cálculo da estatística de erros envolvendo o modelo híbrido (HW e RNA) obteve os menores valores em Fortaleza, São Luís e Natal, por exemplo, com valores de erros percentuais de 3,80%, 4,91% e 2,85%, respectivamente. A redução das variáveis da estatística de erros por parte dos modelos híbridos quando comparado separadamente a utilização dos modelos (ARIMA, HW e RNA) poderá influenciar nas precisões das velocidades dos ventos previstas.

Abstract The objective of this work is to perform time series forecasts of wind speed in terms of monthly averages in the Brazilian Northeast. The following time-series models, Auto-Regressive Integrated Moving Average (ARIMA) and Holt-Winters (HW) were tested, as well as computational artificial intelligence with the use of Artificial Neural Networks (ANN). In addition, two hybrid models were tested, the first with a combination of the ARIMA and ANN models, which work in the literature, and the second is an attempt to combine HW and ANN models. The adjusted series obtained by the hybrid models, are efficient to follow the profile of the observed series of the study regions, with similarities to the data observed in terms of maxima and minima, thus indicating the capacity of the models to represent seasonalities. The calculation of error statistics involving the hybrid (HW and ANN) model obtained the lowest values in Fortaleza, São Luís and Natal, for example, with percent error values of 3.80%, 4.91% and 2.85%, respectively. The reduction of the statistical variables of errors by the hybrid models when comparing the use of the models (ARIMA, HW and ANN) separately may influence the predictions of expected wind velocities.

Abstract: Time series methodology was used in this study to compare different methods of forecasting in yields series of basic grains (maize, beans, wheat and rice) in Mexico, with the objective of predict their values in the short term. Forecasts model was ARIMA (1,0,1) for maize, Brow Model with α = 0.202 for beans, Simple Exponential Smoothing with α = 0.757 for wheat and Holt Model with α = 0.5024 and β = 0.0366 for rice. Results indicate that in the short term yields of maize, beans and rice will increase; while wheat yields remain constant. In addition, under different scenarios of population, keeping constant acreage and per capita consumption of basic grains, Mexico will only be self-sufficient in the production of beans. Forecasts obtained in this study can be used in making production and buy-sale of grains decisions.

Resumen: La metodología de series de tiempo fue empleada en el presente estudio para comparar diferentes métodos de pronósticos en series de rendimiento de granos básicos (maíz, frijol, trigo y arroz) en México, con el objetivo de predecir sus valores en el corto plazo. Los pronósticos se realizaron empleando los modelos Autoregressive Integrated Moving Average (ARIMA) (1,0,1) para maíz, Modelo de Brow con α = 0.202 para frijol, Suavización Exponencial Simple con α = 0.7576 para trigo y Modelo de Holt con α = 0.5024 y β = 0.0366 para arroz. Los resultados indican que en el corto plazo los rendimientos de maíz, frijol y arroz se incrementarán, mientras que los rendimientos de trigo se mantendrán constantes. Respetando estas estimaciones de rendimiento, manteniendo constante la superficie cultivada y el consumo per cápita de granos básicos, y considerando diferentes escenarios de población, a largo plazo México solo será autosuficiente en la producción de frijol. Así, los pronósticos obtenidos en este trabajo pueden ser utilizados en la toma de decisiones de producción y compra-venta de granos.

O monitoramento da umidade relativa do ar (UR) tem grande importância no gerenciamento de recursos hídricos, na área agrícola, em estudos climáticos, assim como para a gestão da saúde pública. Este trabalho tem o objetivo de analisar, modelar e prever os valores mensais de UR da cidade de Brasília, Distrito Federal. Como a UR se dá em termos percentuais, ou seja, assume valores contínuos no intervalo (0,1), os modelos de séries temporais tradicionais da classe ARIMA não são adequados. Com isso, a utilização do modelo beta autorregressivo de médias móveis (βARMA) se faz necessária. Esse modelo para taxas e proporções, que assume distribuição beta para a variável de interesse, foi proposto recentemente por Rocha e Cribari-Neto. Para realização deste trabalho o modelo βARMA foi implementado em linguagem R. Sua aplicação aos dados de UR mostrou-se adequada, captando o comportamento da série e gerando previsões coerentes.

Relative humidity (RH) monitoring is of great importance for the management of water resources, agricultural field, climatic studies, as well as for public health management. The objective of this paper is to analyze, model, and forecast the monthly RH at Brasília city, Distrito Federal, Brazil. RH is given in percentage terms, i.e., assume continuous values in the interval (0,1). In this situation the traditional time series models, like the ARIMA class, are not suitable. Thus, the use of beta autoregressive moving average models (βARMA) is required. This model proposed by Rocha and Cribari-Neto was developed for rates and proportions variable with beta distribution. For this study, the βARMA model was implemented in R language. Its application to RH data was adequate, capturing the behavior of the RH series and producing accurate predictions.

Northeastern Argentina is the forest area of greater importance in the country, concentrated in the provinces of Misiones and Corrientes, with Pinus spp. L., the species with higher production, which supplies raw materials to a large number of industrial activities. This highlights the need to implement forest management tools to make better decisions in investment and management of forests. Forest management models often use different techniques, including simulation, based on operational research, and econometric tools. Usually, the econometric techniques tend to be used for projections of prices and returns. An important class of models with longitudinal data is the family of Autoregressive moving average models, known as ARIMA, by its acronym in English, usually applied to describe trends and generate predictions from values passed from the series. In particular, the variation of prices of forest products is one of the main sources of uncertainty in forest planning. Nevertheless, the application of techniques and prediction models in the forestry area, especially at the South American region is still low. ARIMA Models exhibit good predictive short-term performance, although they lose ability to forecast in distant horizons and have some other disadvantages. Various autoregressive moving average models (ARIMA) based on Box-Jenkins methodology are proposed to predict future prices of four products for Pinus spp manufactured in Northeast Argentina. Estimations were carried out with time series of prices of the four products covering the period July 2002-September 2013. The proposed models predict future prices with forecast errors between 0,9% and 1,8%.

En el noreste argentino se encuentra el polo forestal de mayor importancia del país, concentrado en las provincias de Misiones y Corrientes, siendo Pinus spp. L. la de mayor producción, las cuales abastecen a gran número de actividades industriales. Esto evidencia la necesidad de aplicar herramientas de gestión forestal para tomar mejores decisiones de inversión y manejo de los montes. Los modelos de gestión forestal suelen utilizar distintas técnicas, entre ellas simulación, basadas en investigación operativa, y econométricas. Generalmente, las técnicas econométricas suelen ser utilizadas para proyecciones de precios y retornos. Una clase importante de modelos con datos longitudinales es la familia de los modelos autorregresivos de media móvil, conocidos como ARIMA, por sus siglas en inglés, generalmente aplicados para describir tendencias y generar predicciones a partir de valores pasados de las series. En particular, la variación de precios forestales es una de las principales fuentes de incertidumbre en la planificación forestal. Sin embargo, es escasa aún la aplicación de técnicas y modelos de predicción en el área forestal, especialmente a nivel sudamericano. Los modelos ARIMA exhiben buen desempeño predictivo en el corto plazo, aunque pierden capacidad de pronóstico en horizontes alejados y presentan algunos otros inconvenientes. Se propone un modelo autorregresivo de media móvil (ARIMA) basado en la metodología de Box-Jenkins para predecir los precios de cuatro productos de Pinus spp. para el noreste argentino. Para ello se utilizan series temporales de precios correspondientes al periodo julio 2002-septiembre 2013. Los modelos propuestos predicen precios futuros con errores de predicción entre 0,9% y 1,8%.

This paper compares out-of-sample performance, using the Chilean GDP dataset, of a large number of autoregressive integrated moving average (ARIMA) models with some variations to identify how to achieve the smallest root mean squared forecast error with models based on information criteria-Akaike, Schwarz, and Hannan-Quinn. The analysis also addresses the role of seasonal adjustment and the Easter effect. The results show that Akaike and Schwarz are better criteria for forecasting when using actual series and Schwarz and Hannan-Quinn are better with seasonally adjusted data. Accounting for the Easter effect improves forecast accuracy for actual and seasonally adjusted data.