Análise longitudinal dos preços do carvão vegetal, no Estado de Minas Gerais

Coelho, Luiz Moreira; Rezende, José Luiz Pereira de; Calegário, Natalino; Silva, Márcio Lopes da

doi:10.1590/s0100-67622006000300013

Cited by 14 publications

(6 citation statements)

References 3 publications

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“…Para o estudo de projeção, foi usado o modelo ARIMA (Autorregressivo Integrado de Média Móvel), conhecido também como metodologia de Box & Jenkins (BOX;JENKINS, 1976), bastante difundida no meio científico (SILVA;COELHO JÚNIOR et al, 2006).…”

Section: Methodsunclassified

Projeções Da Produção De Celulose De Fibra Curta No Brasil

Castro¹,

Mendes²,

Ataíde³

et al. 2011

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ResumoCom base em uma série temporal anual de produção de celulose de fibra curta no Brasil no período de 1950 a 2009, o presente trabalho objetivou analisar a eficiência da metodologia Box & Jenkins em prever a produção. O modelo mais adequado foi escolhido com base nos critérios de AIC e SCH, na significância dos coeficientes, no princípio de parcimônia e no comportamento dos resíduos. Pelos resultados, conclui-se que o modelo ARIMA (2,2,1) é adequado para prever a produção de celulose de fibra curta no Brasil. Palavras-chave: Celulose de fibra curta; séries temporais; metodologia Box & Jenkins. Abstract Projections of short fiber cellulose production in Brazil.Based on an annual production series of hardwood pulp in Brazil from 1950 to 2009, this study aimed to analyze efficiency of the Box & Jenkins methodology to forecast production. The most appropriate model was chosen based on the AIC and SCH criteria, on the significance of coefficients, on the principle of parsimony and residual behavior. The results points to the ARIMA (2,2,1) model as the most adequate to forecast the hardwood pulp production in Brazil.

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Section: Methodsunclassified

Projeções Da Produção De Celulose De Fibra Curta No Brasil

Castro¹,

Mendes²,

Ataíde³

et al. 2011

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“…Para compreender como o ambiente se comporta com o passar dos anos, estudiosos utilizam-se de observações de fenômenos e objetos, para assim tentar compreender melhor o ambiente e suas particularidades. Essas observações são chamadas de séries temporais, que é uma coleção de variáveis aleatórias ordenadas no tempo, isto é, são coletadas por um processo estocástico (WOOLDRIDGE, 2002;COELHO JUNIOR et al, 2006;GUJARATI, 2011).…”

Section: Série Temporalunclassified

Descrição temporal do comportamento do Cerrado sensu strictu usando séries temporais

Machado

Silva

Araújo

2018

CeN

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Currently has been a growing concern about the relationship between humans and nature. The challenges to making this relationship more sustainable have been pointed out. Together with this growing need and concern environmental, geotechnologies appear as strong allies, especially in the generation of more accurate and updated data to assist in tasks such as environmental management and planning in the processes of exploration of a given biome. In this context, this study aimed to analyze the phenological behavior of Cerrado phytophysiognomies (Pirapitinga Ecological Station - MG) with the use of time series of NDVI and surface temperature determined from digital images, obtained with the Landsat 5-TM sensor. For this, a study was carried out with the two time series, analyzing the intrinsic variables, such as trend, seasonality, prediction and cointegration. The prediction of the NDVI series presented a standard error lower than 0.079, which represents a quality information about the phytophysiognomy analyzed at a future time. This is a very important fact, given that currently the Cerrado suffers from an accelerated process of degradation. Therefore, it is useful information in processes of recovery, maneuver and management of degraded areas.

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“…Works on charcoal price predictions were written by Silva and Silva (1996), who applied the Box & Jenkins method and found that ARIMA (1,1,1) provided better fit than other conventional methods, although they did not include seasonal effects in the model, while Coelho Junior et al (2006a) fitted seasonality models and found that SARIMA (2,0,1)(0,1,1) 12 provided good fits and a simple and frugal model.…”

Section: Coelho Junior L M Et Almentioning

confidence: 99%

Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais

Coelho

Rezende

Batista

et al. 2013

CERNE

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ABSTRACT:Energy is an important factor of economic growth and is critical to the stability of a nation. Charcoal is a renewable energy resource and is a fundamental input to the development of the Brazilian forest-based industry. The objective of this study is to provide a prognosis of the charcoal price series for the year 2007 by using Artificial Neural Networks. A feedforward multilayer perceptron ANN was used, the results of which are close to reality. The main findings are that: real prices of charcoal dropped between 1975 and 2000 and rose from the early 21st century; the ANN with two hidden layers was the architecture making the best prediction; the most effective learning rate was 0.99 and 600 cycles, representing the most satisfactory and accurate ANN training. Prediction using ANN was found to be more accurate when compared by the mean squared error to other studies modeling charcoal price series in Minas Gerais state.Key words: Forest economics, time series, prediction. USO DE REDES NEURAIS ARTIFICIAIS PARA A PROGNOSE DOS PREÇOS DO CARVÃO VEGETAL EM MINAS GERAIS

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Análise longitudinal dos preços do carvão vegetal, no Estado de Minas Gerais

Cited by 14 publications

References 3 publications

Projeções Da Produção De Celulose De Fibra Curta No Brasil

Projeções Da Produção De Celulose De Fibra Curta No Brasil

Descrição temporal do comportamento do Cerrado sensu strictu usando séries temporais

Use of artificial neural networks for prognosis of charcoal prices in Minas Gerais

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