Article2024

Machine Learning Coffee Price Predictions

148 citations0/0 files storedJournal of Uncertain Systems

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Stock Market Forecasting Methods Economics of Agriculture and Food Markets Forecasting Techniques and Applications

Abstract

Most market players have found great significance in price projections for basic agricultural commodities for a substantial duration. We look at the daily price of coffee that is released in this research in order to tackle the issue. The analytical sample runs from 2 January 2013 to 10 April 2024, a period of more than 12 years. A significant influence on the business sector comes from the price series under investigation. Specifically, in this particular situation, Gaussian process regression models are developed using Bayesian optimization techniques and cross-validation processes. Thus, this circumstance prompts the development of price forecasting methodologies. Using our empirical forecasting technique, we produce relatively accurate price projections for the out-of-sample assessment period, which runs from 3 January 2022 to 10 April 2024. It was found that price forecasts of coffee had a relative root mean square error of 2.0500%. With the availability of price forecasting models, investors and governments can make educated decisions about the coffee market given that they have access to the required data.

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References

1996A test for independence based on the correlation dimension 2006Chapter 4 Forecast Combinations 2020Corn Cash Price Forecasting 2021A hybrid model for carbon price forecastingusing GARCH and long short-term memory network 2021Corn cash price forecasting with neural networks 2024Forecasting wholesale prices of yellow corn through the Gaussian process regression 2024Wholesale price forecasts of green grams using the neural network 2024Price forecasting through neural networks for crude oil, heating oil, and natural gas

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