MACHINE LEARNING APPROACH TO AGRICULTURAL PRICE FORECASTING
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Date
2018-01-01
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Department
Computer Science and Electrical Engineering
Program
Computer Science
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Access limited to the UMBC community. Item may possibly be obtained via Interlibrary Loan thorugh a local library, pending author/copyright holder's permission.
This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
Abstract
The use of machine learning is on a rise in many industries, including the agricultural sector. For example, McCormick and Company is a leading spice manufacturer, and they would like to forecast changes in the price of raw spices. The most commonly used spice in North America is black pepper. In this research, done collaboratively with McCormick and Company, we propose a method to predict the price of black pepper ahead by one and three months. Our data-set is made up of Mintec crop prices, OECD financial data, and weather data from the Weather Underground. We explored classic time series models such as the ARIMA and persistence models. They performed poorly, so we turned to linear and support vector regression. We were able to achieve a mean average precision (MAPE) of 3.67 for the linear SVR one month ahead prediction. Empirical results show which features are most informative for predicting black pepper prices.