摘要
股票价格的预测一直受到金融投资者及学者的广泛关注,同时也是学者的研究重点。股票价格的非线性性、波动性等特点使得使用传统统计学方法进行股票预测的准确率较不理想。长短时记忆循环网络(LSTM)模型在处理时间序列数据上有很大的优势,为了进一步优化预测模型,本文提出了双向传播长短时记忆循环网络(Bi-LSTM),通过双向传播历史数据来对股票价格进行预测。另外,还引入传统神经网络BP、LSTM、GRU与本文所提Bi-LSTM模型进行对比,并引入MSE、RMSE、MAE、R2四种误差评估方法来对模型进行评估,验证模型的预测精度。实证结果表明,基于Adam优化算法的Bi-LSTM网络优于BP、LSTM、GRU等传统预测模型,精确度有明显提升,验证了本模型的有效性和可行性。
Stock price prediction has been widely concerned by financial investors and scholars, and it is also the research focus of scholars. The non-linearity and volatility of stock prices make the accuracy of stock prediction using traditional statistical methods less than ideal. LSTM model has great advantages in processing time series data. In order to further optimize the prediction model, this paper proposes a bidirectional propagating long and short time memory cycling network (Bi-LSTM), which can forecast stock prices by bidirectional propagating historical data. In addition, the traditional neural network BP, LSTM and GRU are introduced to compare with the Bi-LSTM model proposed in this paper, and four error evaluation methods of MSE, RMSE, MAE and R2 are introduced to evaluate the model to verify the prediction accuracy of the model. The empirical results show that the Bi-LSTM network based on ADAM optimization algorithm is superior to BP, LSTM, GRU and other traditional prediction models, and the accuracy is significantly improved, which verifies the effectiveness and feasibility of this model.
出处
《统计学与应用》
2021年第3期538-546,共9页
Statistical and Application
