Machine Learning Coffee Price Predictions
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.
Authors
- Bingzi Jin (ORCID: https://orcid.org/0009-0005-1620-7772)
- Xiaojie Xu (ORCID: https://orcid.org/0000-0002-2158-6958)
Institutions
- North Carolina State University (US)
Publication Details
- Journal
- Journal of Uncertain Systems
- Published
- 2024-09-13
- DOI
- https://doi.org/10.1142/s1752890924500235
- Citations
- 75
- Primary Topic
- Wine Industry and Tourism
- Type
- article
- Field-Weighted Citation Impact
- 69.79