Elaboração de modelo de previsão de preço da madeira de Eucalyptus spp.

Soares, Naisy Silva; Silva, Márcio Lopes da; Rezende, José Luiz Pereira de; Lima, João Eustáquio de; Carvalho, Kaio Henrique Adame de

doi:10.1590/s0104-77602010000100005

CERNE

2010

DOI: 10.1590/s0104-77602010000100005

|View full text |Cite

Elaboração de modelo de previsão de preço da madeira de Eucalyptus spp.

Naisy Silva Soares

Márcio Lopes da Silva

José Luiz Pereira de Rezende

et al.

Abstract: Conduziu-se este trabalho com o objetivo de elaborar um modelo para prever o preço da madeira em pé de Eucalyptus spp. em Itapeva (SP) e Bauru (SP), utilizando a metodologia de Box & Jenkins (1976) em observações mensais, cobrindo o período de maio de 2002 a janeiro de 2008. Os resultados obtidos indicaram que o modelo adequado para efetuar as previsões do preço da madeira de Eucalyptus spp. em Itapeva e Bauru foram, respectivamente, um ARIMA (0,1,4) e um ARIMA (2,1,2).

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2014

2022

Publication Types

Select...

Article4

Relationship

Self Cite0

Independent4

Authors

Journals

Cited by 4 publications

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Modelagem E Prognose Do Preço De Carvão Usando Um Sistema Neuro-Fuzzy

Júnior

Silva

et al. 2016

CERNE

View full text Add to dashboard Cite

RESUMO Utilizando dados da série temporal mensal de preços de carvão vegetal em Minas Gerais no período de janeiro de 2000 à setembro de 2014, este estudo teve como objetivo avaliar o uso do sistema neuro-fuzzy para modelagem e previsão de preços. Foram utilizados quatro estruturas de modelagem considerando diferentes defasagens da variável preço (1, 2, 3, 4 e 5 defasagens). A estrutura mais adequada para o sistema neuro-fuzzy foi escolhido com base nos valores de raiz quadrada do erro médio quadrático, erro médio absoluto, erro médio quadrático, erro médio percentual absoluto e máximo erro percentual absoluto para o período de previsão. Com os resultados encontrados, é possível concluir que um sistema neuro-fuzzy pode ser usado para prever corretamente os preços do carvão vegetal.

show abstract

Modelagem E Prognose Do Preço De Carvão Usando Um Sistema Neuro-Fuzzy

Júnior

Silva

et al. 2016

CERNE

View full text Add to dashboard Cite

show abstract

Sampling Methods and Number of Roundwoods for Determining the Moisture Content

et al. 2018

View full text Add to dashboard Cite

This study had the objective of comparing two methodologies of sampling, Simple Random Sampling (ACS) and Stratified Random Sampling (ACE) to determine the optimum number of roundwood samples to obtain the moisture content of the population. In order to achieve this goal, different percentages of allowable error (5,10,15 and 20%) were considered for each sampling methodology. In the conduction of this study, the samples were randomly taken from a lot of 250 steres of wood, 144 roundwood of three meters of length and distributed in four classes of diameter. Later, the moisture content of these samples was determined. And, from these values, the population estimates (average, standard deviation, variance, coefficient of variation, and standard error) by ACS and ACE methods, helped to determine the optimum number of roundwood (n) to be sampled from different percentages of allowable error adopted in this study at 95% probability. According to the results, the amount of roundwood to be sampled from ACS for each allowable error 5, 10, 15 and 20% was respectively 214, 55, 25 and 14. For the ACE (proportional allocation) the amount of roundwood was 141, 35, 16 and 9 for ACE (optimal allocation) this number was 136, 34, 15 and 8. It was concluded that the most indicated sampling method for this study, considering the allowable error, was the ACE method.

show abstract

Predicción de precios de productos de Pinus spp.con modelos ARIMA

Broz

Viego

2014

MYB

View full text Add to dashboard Cite

ResumenSe propone un modelo autorregresivo de media móvil (ARIMA) basado en la metodología de Box-Jenkins para predecir los precios de 4 productos de Pinus spp para el Noreste Argentino. Para ello se utilizan series temporales de precios correspondientes al período julio 2002-setiembre 2013. Los modelos propuestos predicen precios futuros con errores de predicción entre 0,9% y 1,8%.Palabras claves: media móvil, modelos auTorregresivos, mulTiProducTo ForesTal, Predicción de Precios. AbstRActWe estimate various autoregressive moving average models (ARIMA) based on Box-Jenkins methodology to predict future prices of 4 products for Pinus spp manufactured in Northeast Argentina. Estimations were carried with time series of prices of 4 products covering the period July 2002-September 2013. The proposed models predict future prices with forecast errors between 0,9% and 1,8%.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

hi@scite.ai

334 Leonard St

Brooklyn, NY 11211

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Elaboração de modelo de previsão de preço da madeira de Eucalyptus spp.

Cited by 4 publications

References 2 publications

Modelagem E Prognose Do Preço De Carvão Usando Um Sistema Neuro-Fuzzy

Modelagem E Prognose Do Preço De Carvão Usando Um Sistema Neuro-Fuzzy

Sampling Methods and Number of Roundwoods for Determining the Moisture Content

Predicción de precios de productos de Pinus spp.con modelos ARIMA

Contact Info

Product

Resources

About