A New GEP Algorithm and Its Applications in Vegetable Price Forecasting Modeling Problems

Yang, Lei; Li, Kangshun; Zhang, Wensheng; Kong, Yaolang

doi:10.1007/978-981-10-0356-1_14

Cited by 1 publication

(1 citation statement)

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“…In addition, Yang et al (2016) forecasted vegetable prices and used genetic expression programming in order to do it, yet their results were not very clear. The comparison with the benchmark models (including linear regression, among others) was made on a graph, and no results for statistics such as the root-mean-square error were presented.…”

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 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: Agricultural Commoditiesmentioning

confidence: 99%