Local Wheat Price Prediction Models

Wholesale price forecasts of green grams using the neural network

“…Forecasts of prices of soybean oil (Silalahi, 2013;Li et al, 2020), soybeans (dos Reis Filho et al, 2020Li et al, 2022;Yoosefzadeh-Najafabadi et al, 2021;Ribeiro and dos Santos Coelho, 2020;Zhao, 2021;Jiang et al, 2019;Handoyo and Chen, 2020), wheat (Fang et al, 2020;Ribeiro and dos Santos Coelho, 2020;Kohzadi et al, 1996;Zou et al, 2007;Rasheed et al, 2021;Khamis and Abdullah, 2014;Dias and Rocha, 2019;G omez et al, 2021;Bakhtadze et al, 2021), sugar (Surjandari et al, 2015;Ribeiro and Oliveira, 2011;Zhang et al, 2021;Melo et al, 2007;de Melo et al, 2004;Silva et al, 2019), palm oil (Kanchymalay et al, 2017), corn (Xu and Zhang, 2021a;Mayabi, 2019;Xu and Zhang, 2023g;Moreno and Salazar, 2018;Xu and Zhang, 2023w;Zelingher et al, 2021;Shahhosseini et al, 2021Shahhosseini et al, , 2020dos Reis Filho et al, 2020;Zelingher et al, 2020;Ribeiro et al, 2019;Surjandari et al, 2015;Ayankoya et al, 2016;Wan and Zhou, 2021;Antwi et al, 2022), cotton ...…”

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

“…In recent years, as computing resources and tools are becoming continuously easier and more affordable to reach [100], researchers across the globe have started to show continuously increasing interest in exploring applications of machine learning techniques [101] for the purpose of forecasting commodity prices. 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]…”

“…In recent years, as computing resources and tools are becoming continuously easier and more affordable to reach [100], researchers across the globe have started to show continuously increasing interest in exploring applications of machine learning techniques [101] for the purpose of forecasting commodity prices. 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 [102–108], soybean oil [109–111], palm oil [112], sugar [113–118], corn [102, 113, 119–131], wheat [105, 132–139], coffee [140–146], oats [147], cotton [132, 148], canola [149–151], peanut oil [152–158], green beans [159, 160], and edible oil [112, 153, 161–164], those in the energy sector, such as electricity [165–169], carbon emission allowances [170–174], coal [175–179], crude oil [180–184], heating oil [185–189], and natural gas [190–194], and those in the metal sector, such as lead [195], copper [196], palladium [197–201], platinum […”