Precision Corn Price Prediction with Advanced ML Techniques
Corn Market; Economic Indicators; Forecasting Accuracy; Machine Learning; Price Prediction
In the ever-evolving corn market, accurate price prediction is imperative for informed decision-making. This research introduces an innovative predictive model that integrates and external factors to enhance forecasting accuracy in the corn market. By exploring historical trends, comparing machine learning algorithms, and employing advanced feature selection methods, the study addresses the complexities of the corn market, emphasizing economic indicators, geopolitical events, and demand-supply dynamics. Informed by a literature review, the research underscores the necessity of dynamic models in corn price forecasting. Utilizing machine learning models such as linear regression, random forest, SVM, Adaboost, and ARIMA, coupled with the interpretability of SHAP values, the study aims to improve prediction accuracy in the corn market. With a robust methodology and comprehensive evaluation metrics (MAE, RMSE, MAPE), the research contributes valuable insights into corn market dynamics, providing a variable dictionary for clarity and emphasizing the strategic implications of the superior random forest model for stakeholders in the corn sector. 2024 IEEE.
Gaur S.; Mahajan J.; Sharma M.; Hussain D.; Kakani R.; Saxena A.
TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024
Institute of Electrical and Electronics Engineers Inc.
Gaur S., Christ University, India; Mahajan J., Christ University, Bengaluru, India; Sharma M., Christ University, Bengaluru, India; Hussain D., Christ University, Bengaluru, India; Kakani R., Sg Analytics, Bengaluru, India; Saxena A., Christ University, Bengaluru, India