Futures price prediction of agricultural products based on machine learning

Agricultural product futures are crucial to economic development, and the prediction of agricultural product futures prices has an important impact on the stability of the market economy. In order to improve the accuracy of agricultural product futures price prediction, based on machine learning algorithms, this study mainly uses machine learning methods to predict futures prices based on the analysis of fundamental factors affecting agricultural product futures prices. Moreover, in this study, wavelet analysis method is used to smooth the data and then build a model to process the hierarchical information after signal decomposition. In addition, this study conducts model validity studies through cases to draw comparative statistical diagrams to analyze the accuracy of model prediction data. The research shows that the model proposed in this paper has certain effects and can provide theoretical reference for subsequent related research.