Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm

Luo, Changshou; Wei, Qingfeng; Zhou, Lei; Zhang, Junfeng; Sun, Shuliang

doi:10.1007/978-3-642-18354-6_79

Cited by 25 publications

(15 citation statements)

References 2 publications

(2 reference statements)

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“…In the case of other commodities, it can be seen that bare genetic algorithms were used a little more frequently (Carrasco et al 2020). Besides that, the tools initially introduced in the 2000s or early 2010s (Shahwan and Odening 2007;Yuan 2012;Luo et al 2011;Silalahi 2013), such as hybrids with NNs or simple forecasting methods like the ARIMA, have still been developed within last 5 years (García and Kristjanpoller 2019;Bin Khamis and Yee 2018;Yu et al 2018;Zhao et al 2017;). On the other hand, interest grew in hybrids of genetic algorithms and modern methods widely known as machine learning (Weng et al 2018;Weng et al 2020;Parida et al 2019).…”

Section: Discussionmentioning

confidence: 99%

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A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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This paper is focused on the concise review of the specific applications of genetic algorithms in forecasting commodity prices. Genetic algorithms seem relevant in this field for many reasons. For instance, they lack the necessity to assume a certain statistical distribution, and they are efficient in dealing with non-stationary data. Indeed, the latter case is very frequent while forecasting the commodity prices of, for example, crude oil. Moreover, growing interest in their application has been observed recently. In parallel, researchers are also interested in constructing hybrid genetic algorithms (i.e., joining them with other econometric methods). Such an approach helps to reduce each of the individual method flaws and yields promising results. In this article, three groups of commodities are discussed: energy commodities, metals, and agricultural products. The advantages and disadvantages of genetic algorithms and their hybrids are presented, and further conclusions concerning their possible improvements and other future applications are discussed. This article fills a significant literature gap, focusing on particular financial and economic applications. In particular, it combines three important—yet not often jointly discussed—topics: genetic algorithms, their hybrids with other tools, and commodity price forecasting issues.

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Section: Discussionmentioning

confidence: 99%

“…More ambiguous results were obtained by Luo et al (2011). They forecasted and simulated vegetable prices on the Chinese market.…”

Section: Agricultural Commoditiesmentioning

confidence: 99%

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Drachal

Pawłowski

2021

Economies

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“…This imaginative application isn't helpful for ranchers and shoppers yet in addition helpful for farming arranging, surrounding polices and plans in horticulture and market arranging. Information mining arrangement method, for example, Neural Network plays a significant job in non-straight time arrangement expectation [2][3][4][5]. There are numerous sorts of expectation strategy on premise of Neural System, among them the utilization of BP Neural Network calculation is most significant one.…”

Section: Related Workmentioning

confidence: 99%

Onion Price Prediction Based on Artificial Intelligence

Ratha¹,

Sountharya²,

R³

et al. 2020

Int. Res. J. multidiscip. Technovation

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India being a horticulture nation, its economy prevalently relies upon horticulture yield development and agroindustry items. Information Mining is a developing examination field in onion yield investigation. Yield forecast is a significant issue in horticultural. Any rancher is keen on knowing how a lot yield he is going to anticipate. Break down the different related properties like area, pH esteem from which alkalinity of the dirt is decided. Alongside it, level of supplements like Nitrogen (N), Phosphorous (P), and Potassium (K) Location is utilized along with the utilization of outsider applications like APIs for climate and temperature, sort of soil, supplement estimation of the dirt in that locale, measure of precipitation in the district, soil organization can be decided. Every one of these traits of information will be examined, train the information with different appropriate AI calculations for making a model. The framework accompanies a model to be exact what's more, exact in foreseeing onion yield and convey the end client with appropriate proposals about required manure proportion in light of barometrical and soil parameters of the land which improve to build the harvest yield and increment rancher income.

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“…Agriculture outcome adds value to an Indian economy in that its role is fundamentally instrumental in terms of market, aspect and product contributions. India's economy depends primarily on the growth of agricultural yields and their associated agro-industry [1]. Agricultural area accounts for 60 percentage of Tamilnadu in the total geographical area.…”

Section: Introductionmentioning

confidence: 99%

Developing an Agricultural Product Price Prediction Model using HADT Algorithm

Rȧjeswari¹,

Suthendran²

2019

IJEAT

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Big Data Predictive Analytics and Data mining are emerging recent research field to analyse the agricultural crop price. The applications and techniques of data mining as well as Big Data using agriculture data is considered in this paper. In particular, the farmers are more concern about estimating that how much profit they are about to expect for the chosen crop. As with many other sectors the amount of agriculture data are increasing on a daily source. In this work, agriculture crop price dataset of Virudhunagar District, Tamilnadu, India is considered and for the price prediction model based on data mining decision tree techniques. The main goal is to establish the new predictive model based on Hybrid Association rule-based Decision Tree algorithm (HADT). The outcome for the suggested HADT forecast model is heartening and precise to predict agricultural product prices than other current decision tree models.

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Prediction of Vegetable Price Based on Neural Network and Genetic Algorithm

Cited by 25 publications

References 2 publications

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

A Review of the Applications of Genetic Algorithms to Forecasting Prices of Commodities

Onion Price Prediction Based on Artificial Intelligence

Developing an Agricultural Product Price Prediction Model using HADT Algorithm

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