Machine learning algorithms provide feasibility for crude oil price prediction. In this paper, a novel multi-hybrid predictive neural network model is proposed based on complex deep learning algorithm, which integrates empirical wavelet transform, random inheritance formula error correction algorithm, deep bidirectional LSTM neural network and Elman recurrent neural network with variational learning rate. The prediction model is selected according to the sequence frequency after EWT feature extraction, and the prediction results are obtained by separately predicting and reintegrating. On the basis of individual model, the structure of deep bidirectional training, random inheritance formula and variational learning rate are proposed, which further ameliorate the performance of the model and achieve more effective data information capture. Simultaneously, the examination of variational learning rate provides us with a feasible parameter selection. The proposed model achieves high-precision prediction of crude oil futures price, and stands out in the multi-model comparison analysis and q-DSCID synchronous evaluation, with superior prediction accuracy.

机器学习算法为原油价格预测提供了可行性。本文提出了一种基于复杂深度学习算法的新型多混合预测神经网络模型，该模型将经验小波变换，随机继承公式纠错算法，深度双向LSTM神经网络和具有变学习率的Elman递归神经网络相结合。根据EWT特征提取后的序列频率选择预测模型，并通过分别进行预测和重新整合获得预测结果。在个体模型的基础上，提出了深度双向训练的结构，随机继承公式和变异学习率，进一步改善了模型的性能，实现了更有效的数据信息捕获。同时，变异学习率的检验为我们提供了可行的参数选择。该模型实现了原油期货价格的高精度预测，在多模型比较分析和预测中脱颖而出。q -DSCID同步评估，具有出色的预测精度。