指数趋势预测的BP-LSTM模型

A BP-LSTM trend forecast model for stock index

摘要:

本文根据股指、股价等数据的时序特征将人工神经网络(ANN)与深度学习中的循环神经网络(RNN)引入股指预测，基于BP神经网络模型与长短期记忆(LSTM)神经网络模型构建了BP-LSTM模型.基于上证指数，本文进行了数值实验. 结果表明BP-LSTM预测模型的准确率比传统机器学习模型有明显提升，与普通LSTM模型相比也有较大的提升.

Abstract:

In this paper, according to the time series characteristics of financial data, such as stock index, stock price, etc, we introduce the Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) in deep learning to stock index prediction and build a BP-LSTM model based on the Back Propagation (BP) neural network model and Long Short-Term Memory (LSTM) neural network model. Numerical analysis shows that the accuracy of our model is higher than that of the traditional machine learning models, and it also has some improvement compared with the ordinary LSTM model.