基于深度学习的农产品期货价格预测研究

Research on Price Prediction of Agricultural Futures Using Deep Learning Techniques

提高农产品期货价格的预测能力可为投资者的投资交易和政府宏观调控提供一定借鉴.在对LSTM、GRU、BiLSTM三种深度学习模型进行对比研究的基础上,通过添加随机种子稳定预测结果、使用一阶差分降低价格预测滞后性、用正则化、回调函数等方法解决过拟合问题,对LSTM模型进行优化.利用大连商品交易所农产品期货数据,将优化后的模型应用于玉米、黄大豆1号、鸡蛋三种农产品期货的价格预测.预测结果评价指标表明,优化LSTM模型的均方根误差为17.04,平均绝对误差为13.94,误差分别降低了38.6%和33.6%.优化的深度学习模型能够用于预测农产品期货价格,为投资交易提供借鉴.

Enhancing the predictive capability of agricultural futures prices can offer valuable insights for investors in their transactions and further bolster the theoretical basis for government macroeconomic policies.Through a comparative analysis of three deep learning models(LSTM,GRU,and BiLSTM),the optimization of the LSTM model included the incorporation of random seeds to enhance result stability,utilization of first-order differences to minimize prediction delays,and addressing overfitting issues using regularization techniques and callback functions.Subsequently,the refined model was employed for predicting the prices of three agricultural futures(corn,yellow soybean No.1,and egg)by leveraging data from the Dalian Commodity Exchange.Evaluation of the forecast results indicated that the optimized LSTM model achieved a root mean square error of 17.04 and a mean absolute error of 13.94,resulting in reduced errors of 38.6%and 33.6%,respectively.The enhanced deep learning model developed in this study holds the potential to forecast agricultural futures prices for investment trading purposes.