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- Title
Explainable prediction of feed raw material prices based on machine learning and SHAP.
- Authors
WU Zhan; WANG Chun-xiao
- Abstract
The purpose of the study is to evaluate the performance of machine learning models and to propose an interpretable prediction framework for feed material prices. Soybean meal was selected as the representative raw material of feed products. Based on the monthly settlement price data of soybean meal futures from January 2006 to April 2023, BP neural network, GBDT and XGBoost machine learning algorithms were used to conduct training tests, and then Bayesian optimization algorithm was used to adjust the parameters of each model. Finally, the optimal model and SHAP model are selected to analyze the prediction results. The prediction performance of the BO-XGBoost model proposed in this study is significantly better than that of other benchmark models. The MAPE and R2 of the forecast set are 0.03 and 0.892, indicating a high accuracy of the model. The research shows that the model has a good application prospect, and can provide some reference for the decision making of feed-related enterprise managers and relevant departments.
- Publication
Feed Research, 2023, Vol 46, Issue 23, p178
- ISSN
1002-2813
- Publication type
Article
- DOI
10.13557/j.cnki.issn1002-2813.2023.23.034