Predicting Palm Oil Price Direction using Random Forest

Myat, Aung; Tun, Myint Thu Zar

doi:10.1109/ictke47035.2019.8966799

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…Some studies have applied ML techniques to enhance retail operations. For instance, as indicated in Table 7, Myat and Tun [109] used the RF classification model to predict palm oil prices in Myanmar using data obtained from the Myanmar Edible Oil Dealers Association (MEODA). The prediction was conducted to determine whether the price will rise so that imported palm oil can be traded in the local markets.…”

Section: Applications During Retailmentioning

confidence: 99%

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

Opara,

Okolie

et al. 2024

Plants

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The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.

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Section: Applications During Retailmentioning

confidence: 99%

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

Opara,

Okolie

et al. 2024

Plants

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“…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 […”

Section: Introductionmentioning

confidence: 99%

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

Zhang

2023

Ironmaking & Steelmaking

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There is little doubt about importance of forecasting commodity prices to policy makers and diverse varieties of market participants. In this present work, we analyse price forecasting problems for scrap steel for east, north, south, central, northeast, and southwest China and at the national level with daily data during 08/23/2013-04/15/2021. We focus on exploration of usefulness of non-linear autoregressive neural networks and examine forecasting performance based upon a variety of settings over algorithms for model estimations, numbers of hidden neurons and delays, and ratios for splitting the data into training, validation, and testing phases. We arrive at relatively simple models for the seven price series, which lead to forecasts of high accuracy and stabilities with relative root mean square errors below 0.85%. Our results could serve as standalone technical forecasts. They could be combined with other forecasts for forming perspectives of price trends and carrying out policy analysis.

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Edible Oil Price Forecasting: A Novel Approach with Group Temporal Convolutional Network and BetaAdaptiveAdam

Yang,

Li,

et al. 2024

Communications in Computer and Information Science

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Predicting Palm Oil Price Direction using Random Forest

Cited by 10 publications

References 5 publications

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review

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

Edible Oil Price Forecasting: A Novel Approach with Group Temporal Convolutional Network and BetaAdaptiveAdam

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