Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand

Puchalsky, Weslly; Ribeiro, Gabriel Trierweiler; Veiga, Claudimar Pereira da; Freire, Roberto Zanetti; Coelho, Leandro dos Santos

doi:10.1016/j.ijpe.2018.06.010

Cited by 49 publications

(32 citation statements)

References 57 publications

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“…It is evident that the detection of outliers and their treatment is important to obtain a better fit and a smaller prediction error, because the forecast models are based on the assumption of outliers pretreatment (Veiga et al 2010;Bashiri and Moslemi 2013;Puchalski et al 2018). Ghomi and Sogandi (2018) showed that many actual production processes are contaminated by a continuos stream in correlated data.…”

Section: Figmentioning

confidence: 99%

Control chart based on residues: Is a good methodology to detect outliers?

et al. 2019

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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.

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Section: Figmentioning

confidence: 99%

Control chart based on residues: Is a good methodology to detect outliers?

et al. 2019

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“…Mohammed El‐Diasty et al developed a hybrid harmonic analysis and wavelet neural network model, and sea water level data from four tide gauges were employed to study the performance of the proposed prediction model, and the simulation analysis results showed that the proposed model has better prediction effect . Weslly Puchalsky proposed a prediction based on wavelet neural network to predict the variations in the price of products and services, and the wavelet neural networks performance combined with five optimization technologies is evaluated, and the affecting factors of prediction precision for wavelet neural network are obtained . The wavelet neural network also exists some flaws and deficiencies, it is impossible to deal with the high dimensional function classes of nonpoint singularities, and the phenomenon of singular diffusion occurs when it approaches the results of such objective functions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…5 Weslly Puchalsky proposed a prediction based on wavelet neural network to predict the variations in the price of products and services, and the wavelet neural networks performance combined with five optimization technologies is evaluated, and the affecting factors of prediction precision for wavelet neural network are obtained. 6 The wavelet neural network also exists some flaws and deficiencies, it is impossible to deal with the high dimensional function classes of nonpoint singularities, and the phenomenon of singular diffusion occurs when it approaches the results of such objective functions. In addition, the wavelet neural network needs to increase the number of nodes when dealing with high dimensional data, and then the neural network computing complexity will be improved.…”

mentioning

confidence: 99%

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Zhao

Ren

Gao

et al. 2019

Quality & Reliability Eng

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The proper maintenance plan should be made for ensuring the safety and reliability of polypropylene plant and improve economic benefits of petrochemical enterprise. To meet the requirement, a novel maintenance prediction model of polypropylene plant based on fuzzy theory, ridgelet an artificial neural network is constructed. The economy and reliability models of polypropylene plant maintenance are established through comprehensively considering the reliability and economy. The basic structure of fuzzy ridgelet neural network is designed, and the training algorithm is improved through combining the traditional particle swarm algorithm and bacterial foraging algorithm, and the corresponding algorithm flow is confirmed. Finally, prediction simulation analysis is carried out using a polypropylene plant as research object, and analysis results show that the fuzzy ridgelet neural network has best prediction effect, and the optimal maintenance plan can be confirmed to ensure security and reduce maintenance cost of polypropylene plant.

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“…Aliado à importância do Agronegócio, o setor traz consigo diversas fontes de incerteza, dificuldades e peculiaridades, as quais se apresentam nas cadeias de suprimentos e de distribuição ligados à sazonalidade, à perecibilidade, ao prazo de entrega dos produtos agropecuários (BEHZADI et al, 2017), à difícil previsibilidade dos preços agrícolas (PUCHALSKY et al, 2018), à necessidade de aumento da participação de capital nacional para o negócio e da agricultura familiar (MEDINA; SANTOS, 2017) e a gargalos logísticos (LOPES et al, 2017). Tais fatores fazem com que exista a necessidade de estratégias eficazes e eficientes (BEHZADI et al, 2017).…”

Section: Transformações No Agronegócio Brasileiro: O Surgimento Dos Cunclassified

Condomínios Rurais: revisão sistemática da literatura internacional

Filippi

Guarnieri

Cunha

2019

ESA

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A agricultura sofre com intensas mudanças a cada dia, provenientes da esfera econômica, social, política e tecnológica. As organizações rurais necessitam então se transformar para atender a um cenário dinâmico e um mercado cada vez mais exigente e competitivo (BUAINAIN; GARCIA, 2015; BUAINAIN; GARCIA; VIEIRA FILHO, 2018; SAES; SILVEIRA, 2014). É nesse sentido que os Condomínios Rurais ganham destaque. Os Condomínios Rurais são organizações de produtores rurais vizinhos e/ou próximos, que visam desenvolver atividades agrícolas e/ou pecuárias em conjunto, geralmente com o auxílio tecnológico, e proporcionam obter melhores ganhos e benefícios, superar dificuldades no campo e aumentam a competitividade no Agronegócio (FILIPPI, 2017). O tema é relativamente recente, com pouca literatura acadêmica acerca do assunto e pertinente para o Agronegócio. E também exibe pluralidade e

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Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand

Cited by 49 publications

References 57 publications

Control chart based on residues: Is a good methodology to detect outliers?

Control chart based on residues: Is a good methodology to detect outliers?

Fuzzy ridgelet neural network prediction model trained by improved particle swarm algorithm for maintenance decision of polypropylene plant

Condomínios Rurais: revisão sistemática da literatura internacional

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