Corn Price Prediction Model Using Deep Learning
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Abstract
Animal feed corn is a highly demanded agricultural commodity across various industries. However, its prices fluctuate unpredictably each year, causing difficulties for farmers in planning their crops. Therefore, accurate price forecasting is crucial for helping farmers plan effectively. This research presents the development of a corn price prediction model using both historical price data and other important features. The study compares the performance of four models: ARIMA, ARIMAX, LSTM, and GRU, using data from 2015 to 2021 to predict corn prices for 2020 and 2021. The comparison between models that use only historical price data and those that incorporate additional features shows that the GRU model, utilizing both historical price data and features such as total corn exports, export prices, Chicago Board of Trade futures prices, and total export value, performs the best. The GRU model achieves an RMSE of 0.0780 and an MAE of 0.0662, demonstrating the highest accuracy in the test datasets. Accurate price forecasting is crucial for farmers and stakeholders in the corn industry, as it enables more efficient planning and resource management.
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