Previsão de preços de commodities com modelos ARIMA-GARCH e redes neurais com ondaletas: velhas tecnologias – novos resultados

Lima, Fabiano Guasti; Kimura, Herbert; Neto, Alexandre Assaf; Perera, Luiz Carlos Jacob

doi:10.1016/s0080-2107(16)30537-4

Cited by 9 publications

(11 citation statements)

References 8 publications

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“…Other studies (LIMA et al (2010); RIBEIRO et al (2010);MIRANDA et al (2013)) based on data from the national and international agricultural market (SHAHWAN & ODENING, 2007;LI et al, 2010;JHA;SINHA, 2013), indicated the ANN model as an alternative in forecasting agricultural prices.…”

Section: Resultsmentioning

confidence: 98%

“…In the literature (YONENAGA & FIGUEREDO, 1998;BRESSAN, 2004;SOBREIRO et al, 2008;LIMA et al, 2010;RIBEIRO et al, 2010;CERETTA et al, 2010;FERREIRA et al, 2011), the artificial neural networks (ANN) and classic (autoregressive) models are applied in the context of the Brazilian market to identify seasonal patterns and predict prices of agricultural products. In addition to these applications for the international market of agricultural commodities, some authors (SHAHWAN & ODENING, 2007;LI et al, 2010;JHA & SINHA, 2013), use the ANN model as an alternative to forecast agricultural prices.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market

Pinheiro

Senna

2017

Cienc. Rural

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The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.

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Section: Resultsmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market

Pinheiro

Senna

2017

Cienc. Rural

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“…The model is adaptable to the time series and differs from classical forecasting models for being a nonparametric model and involving learning algorithms (LIMA et al, 2010).…”

Section: Ann Modelmentioning

confidence: 99%

“…The error is used as an interactive parameter of weights adjustment whose intention is to gradually reduce the error to a minimum acceptable value. The back-propagation algorithm is widely used for supervised learning (LIMA et al, 2010).…”

Section: Supervised Learningmentioning

confidence: 99%

“…In research by Lima et al (2010) and Ceretta, Righi and Schlender (2010) in order to investigate the behavior of soybean prices -the former based on the model ARIMA-GARCH / ANN, and the latter comparing the model ARIMA with ANN -, for the authors, the forecast results were favorable to the models ARIMA-GARCH and ANN. Different from research, the results indicate predictive superiority of the M-SSA model.…”

Section: Conclusion and Suggestionsmentioning

confidence: 99%

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Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BM&Fbovespa

Pinheiro¹,

Senna²

2016

BBR

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This study aimed to forecast the prices of a group of commodities through the multivariate spectral analysis model and compare them with those obtained by classical forecasting and neural network models. The choice of commodities such as ethanol, cattle, corn, coffee and soy was due to the emphasis in the exports in 2013. The multivariate spectral model has proved to be suitable, when compared with others, by enabling a better predictive performance. The results obtained in the out-of-sample period, through the use of measurement error and statistical test, confirm this. This research may help market professionals in formulating and implementing policies targeted to the agricultural sector due to the relevance of price forecast as a planning instrument and analysis of the finance market behavior for those who need protection against price fluctuations.

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Previsão do Preço de Energia no Mercado de Curto Prazo: Uma análise Combinada de Séries Temporais e Redes Neurais Artificiais

Belentani

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Atualmente o Brasil e o mundo passam por uma transição energética, cuja economia de baixo carbono tem se efetivado através de fontes de energia cada vez mais limpas e com operações industriais mais eficientes. Além dos ganhos para o meio ambiente, esse caminho deve trazer benefícios econômicos para os agentes do sistema elétrico brasileiro através das diversas oportunidades que serão geradas.Neste contexto a possibilidade de prever os preços de energia elétrica no mercado de curto prazo (MCP), ambiente de contratação livre, pode contribuir significativamente para a otimização do planejamento de contratação da demanda e uma melhor avaliação dos riscos, seja para os agentes geradores ou para a indústria e grandes consumidores de modo geral.Dessa maneira, buscando contribuir com o aumento de previsibilidade dos agentes que atuam no mercado livre de energia elétrica brasileiro, o presente estudo se propõe prever o valor do Preço de Liquidação das Diferenças (PLD) para quatro semanas operativas subsequentes. Para isso, a ideia é utilizar uma combinação entre modelos AutoRegressivos Integrados de Médias Móveis com Sazonalidade (SARIMA) e redes neurais artificiais recorrentes (LSTM). Primeiramente, serão previstos as quatro semanas operativas subsequentes para as variáveis preditoras e então alimentar uma Rede Neural Artificial (RNA) para produzir o PLD.A metodologia traz como resultado previsões com níveis de acuracidade satisfatórios, principalmente pelo fator combinatório de cada técnica.Palavras-chave: Preço da Liquidação das Diferenças, ambiente de contratação livre, mercado de energia elétrica brasileiro, inteligência artificial, redes neurais artificias, LSTM, séries temporais, ARIMA, SARIMA, modelagem combinada.

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Previsão de preços de commodities com modelos ARIMA-GARCH e redes neurais com ondaletas: velhas tecnologias – novos resultados

Cited by 9 publications

References 8 publications

Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market

Multivariate analysis and neural networks application to price forecasting in the Brazilian agricultural market

Price Forecasting Through Multivariate Spectral Analysis: Evidence for Commodities of BM&Fbovespa

Previsão do Preço de Energia no Mercado de Curto Prazo: Uma análise Combinada de Séries Temporais e Redes Neurais Artificiais

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