Efficient Predictive Model for Determining Critical Factors Affecting Commodity Price: The Case of Coffee in Ethiopian Commodity Exchange (ECX)

Abstract

In this paper, we have focused on the data mining technique on market data to establish meaningful relationships or patterns to determine the determinate critical factors of commodity price. The data is taken from Ethiopia commodity exchange and 18141 data sets were used. The dataset contains all main information. The hybrid methodology is followed to explore the application of data mining on the market dataset. Data cleaning and data transformation were used for preprocessing the data. WEKA 3.8.1 data mining tool, classification algorithms are applied as a means to address the research problem. The classification task was made using J48 decision tree classification algorithms, and different experimentations were conducted. The experiments have been done using pruning and unpruning for all attributes. The developed models were evaluated using the standard metrics of accuracy, ROC area. The most effective model to determine the determinate critical factors for the commodity has an accuracy of 88.35% and this result is a good experiment result. The output of this study is helpful to support decisionmaking activities in the area of the Ethiopia Commodity Exchange. The study support commodity suppliers to take care of the determinant factors work towards maintaining quality. Ethiopia Commodity Exchange (ECX), as the main facilitator of commodity exchanges, can also use the model for setting price ranges and regulations.

Yellow corn wholesale price forecasts via the neural network

“…Some typical models sought in previous studies include the ARIMA model, VAR model and VECM model. Over the past decade, computational power has becoming much more affordable, and the interest among researchers in building machine learning models aiming at offering good forecasts in economics and finance has been well documented (Ge, Jiang, He, Zhu, & Zhang, 2020;Yang & Wang, 2019), including, of course, forecasts of commodity prices for the agricultural market (Abreham, 2019;Ali, Deo, Downs, & Maraseni, 2018;Antwi, Gyamfi, Kyei, Gill, & Adam, 2022;Ayankoya, Calitz, & Greyling, 2016;Degife & Sinamo, 2019;Deina et al, 2021;Dias & Rocha, 2019;Fang, Guan, Wu, & Heravi, 2020;Filippi et al, 2019;G omez, Salvador, Sanz, & Casanova, 2021;Handoyo & Chen, 2020;Harris, 2017;Huy, Thac, Thu, Nhat, & Ngoc, 2019;Jiang, He, & Zeng, 2019;Khamis & Abdullah, 2014;Kohzadi, Boyd, Kermanshahi, & Kaastra, 1996;Kouadio et al, 2018;Li, Chen, Li, Wang, & Xu, 2020, Li, Li, Liu, Zhu, & Wei, 2020Lopes, 2018;Mayabi, 2019;de Melo, J unior, & Milioni, 2004;Melo, Milioni, & Nascimento J unior, 2007;Moreno et al, 2018;Naveena et al, 2017;Rasheed, Younis, Ahmad, Qadir, & Kashif, 2021;dos Reis Filho, Correa, Freire, & Rezende, 2020;Ribeiro & Oliveira, 2011;Ribeiro, Ribeiro, Reynoso-Meza, & dos Santos Coelho, 2019;Ribeiro & dos Santos Coelho, 2020;RL & Mishra, 2021;…”

“…Some typical models sought in previous studies include the ARIMA model, VAR model and VECM model. Over the past decade, computational power has becoming much more affordable, and the interest among researchers in building machine learning models aiming at offering good forecasts in economics and finance has been well documented (Ge, Jiang, He, Zhu, & Zhang, 2020; Yang & Wang, 2019), including, of course, forecasts of commodity prices for the agricultural market (Abreham, 2019; Ali, Deo, Downs, & Maraseni, 2018; Antwi, Gyamfi, Kyei, Gill, & Adam, 2022; Ayankoya, Calitz, & Greyling, 2016; Bayona-Oré, Cerna, & Hinojoza, 2021; Degife & Sinamo, 2019; Deina et al. , 2021; Dias & Rocha, 2019; Fang, Guan, Wu, & Heravi, 2020; Filippi et al.…”

“…, 2021; Zhang et al. , 2021; Zhao, 2021), decision tree (Abreham, 2019; Degife & Sinamo, 2019; Dias & Rocha, 2019; Harris, 2017; Lopes, 2018; Surjandari et al. , 2015; Zelingher et al.…”

“…, 2020, 2021), soybean oil (Li et al ., 2020; Silalahi et al. , 2013; Xu & Zhang, 2022), coffee (Abreham, 2019; Degife & Sinamo, 2019; Deina et al. , 2021; Huy et al.…”

Scrap steel price forecasting with neural networks for east, north, south, central, northeast, and southwest China and at the national level

“…Corresponding studies in the literature have covered many different commodities from different economic sectors and industries, including but not limited to those in the agricultural sector, such as soybeans [102][103][104][105][106][107][108], soybean oil [109][110][111], palm oil [112], sugar [113][114][115][116][117][118], corn [102,113,[119][120][121][122][123][124][125][126][127][128][129][130][131], wheat [105,[132][133][134][135][136][137][138][139], coffee [140][141][142][143][144][145][146], oats…”