融合CNN-BiLSTM-Attention的集成学习价格预测

价格预测对于大宗农产品市场的稳定具有重要意义,但是大宗农产品价格与多种因素有着复杂的相关关系.针对当前价格预测中对数据完整性依赖性强与单一模型难以全面利用多种数据特征等问题,提出了一种将基于注意力机制的卷积双向长短期记忆神经网络(CNN-BiLSTM-Attention)、支持向量机回归(SVR)与LightGBM组合的增强式集成学习方法,并分别在包含历史交易、天气、汇率、油价等多种特征数据的数据集上进行了实验.实验以小麦和棉花价格预测为目标任务,使用互信息法进行特征选择,选择误差较低的CNN-BiLSTM-Attention模型作为基模型,与机器学习模型通过线性回归进行增强式集成学习.实验结果表明该集成学习方法在小麦及棉花数据集上预测结果的均方根误差(RMSE)值分别为12.812, 74.365,较之3个基模型分别降低11.00%, 0.94%、4.44%,1.99%与13.03%, 4.39%,能够有效降低价格预测的误差.

Emsemble Learning Fused with CNN-BiLSTM-Attention for Price Forecasting

Price forecasting is important for the stability of bulk agricultural commodity markets, but bulk agricultural commodity prices have complex correlations with multiple factors. In order to address the current problems of strong dependence on data integrity and the difficulty of single models to fully utilize multiple data features in price forecasting, a boosting ensemble learning method that combines the attention mechanism-based convolutional bi-directional long short-term memory neural network (CNN-BiLSTM-Attention), support vector regression (SVR), and LightGBM is proposed, and experiments are conducted on the datasets containing historical trades, weather, exchange rate, oil price, and other features data, respectively. The experiment takes the price forecasting of wheat and cotton as the target task, uses the mutual information method for feature selection, selects the CNN-BiLSTM-Attention model with low error as the base model, and performs boosting ensemble learning with the machine learning model through linear regression. The experimental results show that the root mean square error (RMSE) of the ensemble learning method is 12.812 and 74.356 for wheat and cotton datasets, which are 11.00%, 0.94%, 4.44%, 1.99%, 13.03%, and 4.39% lower than the three base models, respectively. The method can effectively improve the accuracy of price forecasting.