PCC-BiLSTM-GRU-Attention组合模型预测方法

传统预测模型在处理多元时间序列时, 常常难以捕捉其非线性动力系统的复杂变化规律导致预测精度较低. 针对此问题, 本文将PCC-BiLSTM-GRU-Attention组合模型的预测方法进行了探讨和验证. 该方法首先使用Pearson相关系数(PCC)进行相关性检验并删除无关特征, 实现了对多元数据的降维选优. 其次使用双向长短期记忆神经网络(BiLSTM)双向提取时序特征. 最后使用GRU神经网络融合注意力机制(Attention), 进一步学习双向时序特征的变化规律, 精准捕捉关键时刻的信息. 为了验证该方法在多元时间序列中的可行性, 本文以股票价格预测作为实验场景, 并与BP模型、LSTM模型、GRU模型、BiLSTM-GRU模型、BiLSTM-GRU-Attention模型进行对比. 验证结果表明: 本文探讨的PCC-BiLSTM-GRU-Attention组合模型的预测方法相比其他模型具有较高的预测精度, 其平均绝对百分比误差(MAPE)达到了2.484%, 决定系数达到了0.966.

Prediction Method of PCC-BiLSTM-GRU-Attention Combined Model

When dealing with multivariate time series, traditional prediction models are often difficult to capture the complex variation of nonlinear dynamic systems, which results in low prediction accuracy. To solve this problem, this study discusses and verifies the prediction method of the PCC-BiLSTM-GRU-Attention combined model. In the method, Pearson correlation coefficient (PCC) is first used for correlation tests and irrelevant features are deleted to achieve dimensionality reduction and optimization of multivariate data. Then, bidirectional long short-term memory (BiLSTM) neural network is used to extract time series features. Finally, GRU neural network is integrated with the attention mechanism to further learn the change rule of bidirectional time series features and accurately capture the critical moment information. To verify the feasibility of this method in multivariate time series, this study takes stock price prediction as the experimental scene and compares it with the BP model, LSTM model, GRU model, BiLSTM-GRU model and BiLSTM-GRU-Attention model. The verification results show that the prediction method of the PCC-BiLSTM-GRU-Attention combined model has higher prediction accuracy than other models, with the mean absolute percentage error (MAPE) reaching 2.484% and the determination coefficient 0.966.