A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Zhang, Yongli; Na, Sanggyun

doi:10.1155/2018/2540681

Cited by 25 publications

(16 citation statements)

References 32 publications

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“…The results showed that the error was small. Zhang and Na (2018) transformed the time series data of agricultural product price index into Low, R, and Up fuzzy information granulation particles to indicate the trend and magnitude of price movement, and then established the MEA-optimized SVR to predict the fluctuation range and change trend of the price index. The empirical analysis showed that the MEA-SVM had higher prediction accuracy and faster calculation speed.…”

Section: The Forecasting Methods Of Agricultural Product Pricesmentioning

confidence: 99%

Agricultural product price forecasting methods: research advances and trend

Wang

Feng

Sui

et al. 2020

BFJ

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PurposeThe purpose of this paper is to provide reference for researchers by reviewing the research advances and trend of agricultural product price forecasting methods in recent years.Design/methodology/approachThis paper reviews the main research methods and their application of forecasting of agricultural product prices, summarizes the application examples of common forecasting methods, and prospects the future research directions.Findings1) It is the trend to use hybrid models to predict agricultural products prices in the future research; 2) the application of the prediction model based on price influencing factors should be further expanded in the future research; 3) the performance of the model should be evaluated based on DS rather than just error-based metrics in the future research; 4) seasonal adjustment models can be applied to the difficult seasonal forecasting tasks in the agriculture product prices in the future research; 5) hybrid optimization algorithm can be used to improve the prediction performance of the model in the future research.Originality/valueThe methods from this paper can provide reference for researchers, and the research trends proposed at the end of this paper can provide solutions or new research directions for relevant researchers.

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Section: The Forecasting Methods Of Agricultural Product Pricesmentioning

confidence: 99%

Agricultural product price forecasting methods: research advances and trend

Wang

Feng

Sui

et al. 2020

BFJ

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“…Their forecasting has been carried out actively and become a concern using various methods such as statistical methods and artificial intelligence (AI) which refers to any machine that is able to replicate human cognitive skills in problem-solving. Various works related to agricultural price forecasting were reported [14], [15], [16], [17]. In Yu et al [16], a backpropagated (BP) neural network with genetic algorithm (GA) as optimization technique was applied.…”

Section: Ijeei Issn: 2089-3272 mentioning

confidence: 99%

“…The issue of uncertainties is caused by random factors in agricultural commodity market [17]. Here, a mind evolutionary algorithm (MEA) was combined with support vector machine (SVM).…”

Section: Ijeei Issn: 2089-3272 mentioning

confidence: 99%

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

Raflesia¹,

Taufiqurrahman²,

Iriyani³

et al. 2021

IJEEI

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Agricultural commodity price forecasting becomes important for farmers since the knowledge of agriculture commodity price fluctuation can help the farmers to identify the right selling time. Recently, the absence of such the forecasting system makes the farmers decide to sell their commodities to middlemen which in turn, reduces their exchange rate as the length of distribution flow is complicated. The length of distribution flow is started from farmers, middlemen, wholesalers, retailers, and consumers. To address this problem, a forecasting system based on radial basis function neural network (RBFNN) is proposed. To optimize the network's learning process, particle swarm optimization (PSO)-based learning technique is applied. The RBFNN is chosen because of its ability to generally track irregular signal changing, good speed in learning process, and robustness. Meanwhile, the implementation of PSO aims to improve weight values towards global optimum in RBFNN model.

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“…What are the forecasts for the future? Proposed approach for this problem is Artificial Intelligence based solution so as to analyse the past year's data of the cotton crop cultivation (respective to the regiona particular market) and analysing the current trends and developing a model that will help the fellow market traders to predict the future market trends i.e., to suggest a suitable pricing Model [10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

An AI Based Web Portal for Cotton Price Analysis and Prediction

Bohara¹,

Patel²,

Patel³

et al. 2021

Atlantis Highlights in Computer Sciences

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Artificial Intelligence based Web portal is extensively used in worldwide because it used to solve various primary low level problems in agriculture such as predicting precise prices of agriculture product using machine learning algorithms, enabling direct connection between buyer and seller without any middlemen, decreasing wastage of post-harvest crop, fixing appropriate price margin for farmers and so on. These steps helps to increase the capital income and creates the beneficial changes in the agriculture and also it leads to take the agriculture to next level. Cotton is a crop that is possibly the most expensive including all agricultural products sold. Owing to the high uncertainty of cotton markets, it is incredibly difficult to forecast future demand patterns and develop an acceptable pricing plan in order to compete in the market and maximize the company's volume for long-term development. Keeping in mind all the factors stated above for the cultivation of cotton, conclusion can be inferred as the cotton is highly volatile in nature. In this paper, we tend to build an Artificial Intelligence based Web Portal which provides data analysis of price of past years and also gives predictions about the cotton prices to the fellow traders. The paper aims to create a web model for cotton price prediction. Furthermore, the study tries to run a multiple regression model based on different variables to estimate the price of cotton.

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A Novel Agricultural Commodity Price Forecasting Model Based on Fuzzy Information Granulation and MEA-SVM Model

Cited by 25 publications

References 32 publications

Agricultural product price forecasting methods: research advances and trend

Agricultural product price forecasting methods: research advances and trend

Agricultural Commodity Price Forecasting using PSO-RBF Neural Network for Farmers Exchange Rate Improvement in Indonesia

An AI Based Web Portal for Cotton Price Analysis and Prediction

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