Developing a hybrid forecasting system for agricultural commodity prices (case study: Thailand rice free on board price)

Menhaj, MohammadHossein; Kavoosi‐Kalashami, Mohammad

doi:10.1590/0103-8478cr20201128

Cited by 3 publications

(1 citation statement)

References 18 publications

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“…Regional fruit price forecasting is commonplace, and it is crucial to business decision-making in those areas. According to the characteristics of various agricultural products, the type of forecasting can be either long-term [2] or short-term [3][4][5][6]. However, for fruits, it is mostly short-term, based on seasonality, periodicity, and perishability.…”

Section: Literature Overviewmentioning

confidence: 99%

Characteristic mango price forecasting using combined deep-learning optimization model

Jin²,

Huang³

et al. 2023

PLoS ONE

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Accurate product price forecasting is helpful for scientific decision-making and precise industrial planning. As a characteristic fruit that drives regional development, mango price prediction is of great significance to several economies. However, owing to the strong volatility of mango prices, forecasting is vulnerable to uncertainties and is very challenging. In this study, a deep-learning combination forecasting model based on a back-propagation (BP) long short-term memory (LSTM) neural network is proposed. Using daily mango price data from a large fruit wholesale trading center in China from January 2nd, 2014, to April 18th, 2022, mango price changes are learned and predicted to support the fruit industry. The results show that the root mean-square error, mean absolute percentage error, and the R2 determination coefficient of the BP-LSTM combination model are 0.0175, 0.14%, and 0.9998, respectively. The prediction results of the combined model are better than those of the separate BP and LSTM models. Furthermore, it best fits the actual price profile and has better generalizability.

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Section: Literature Overviewmentioning

confidence: 99%

Characteristic mango price forecasting using combined deep-learning optimization model

Jin²,

Huang³

et al. 2023

PLoS ONE

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Various optimized machine learning techniques to predict agricultural commodity prices

Sari,

Duran,

Kutlu

et al. 2024

Neural Comput & Applic

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Recent increases in global food demand have made this research and, therefore, the prediction of agricultural commodity prices, almost imperative. The aim of this paper is to build efficient artificial intelligence methods to effectively forecast commodity prices in light of these global events. Using three separate, well-structured models, the commodity prices of eleven major agricultural commodities that have recently caused crises around the world have been predicted. In achieving its objective, this paper proposes a novel forecasting model for agricultural commodity prices using the extreme learning machine technique optimized with the genetic algorithm. In predicting the eleven commodities, the proposed model, the extreme learning machine with the genetic algorithm, outperforms the model formed by the combination of long short-term memory with the genetic algorithm and the autoregressive integrated moving average model. Despite the fluctuations and changes in agricultural commodity prices in 2022, the extreme learning machine with the genetic algorithm model described in this study successfully predicts both qualitative and quantitative behavior in such a large number of commodities and over such a long period of time for the first time. It is expected that these predictions will provide benefits for the effective management, direction and, if necessary, restructuring of agricultural policies by providing food requirements that adapt to the dynamic structure of the countries.

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Multidimensionality of agricultural grain road freight price: a multiple linear regression model approach by variable selection

Macarringue,

Oliveira,

Dias

et al. 2024

Cienc. Rural

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The road system is the main mode used for the transportation of agricultural cargo, and in some cases, it is the only option for handling this type of product. This dependence means that the implementation of tools to support the management of logistical costs can reduce the financial impact with the transport felt by the economic agents operating in the soybean chain. This study contributed to a better understanding of the variables that make up the cost of road freight, generating a system of road freight prediction from a multiple linear regression model using the selection of variables Stepwise, Forward, and Backward elimination. This being said, this research intends to evaluate whether the behavior of soybean road freight is influenced by the variables that make up the productive, economic, and infrastructure dimensions in price formation. The regression models had an explanatory power of 87.20%. In the infrastructure dimension, the most impact variable in soybean road freight was the distance traveled; in the economic dimension, the variables of inflation and fuel price stood out; while in the productive dimension, the main contribution was the volume of production. A more assertive predictability of logistical costs and better understanding of the dynamics of freight price formation helps industry agents in planning and decision-making. Another contribution of this study is that it can be used as a practical tool for predicting soybean road freight on several transportation routes.

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Developing a hybrid forecasting system for agricultural commodity prices (case study: Thailand rice free on board price)

Cited by 3 publications

References 18 publications

Characteristic mango price forecasting using combined deep-learning optimization model

Characteristic mango price forecasting using combined deep-learning optimization model

Various optimized machine learning techniques to predict agricultural commodity prices

Multidimensionality of agricultural grain road freight price: a multiple linear regression model approach by variable selection

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