指数趋势预测的BP-LSTM模型

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，本文进行了进行数值实验.结果表明BP-LSTM预测模型的准确率相比传统机器学习模型有明显提升，与普通LSTM模型相比也有较大提升.

A BP-LSTM trend forecast model for stock index

Neural network plays a very important role in stock market trend prediction. According to the time series characteristics of financial data such as stock index and stock price, this paper introduce Artificial Neural Network (ANN) and Recurrent Neural Network (RNN) in deep learning to stock index prediction and builds BP-LSTM model based on Back Propagation (BP) neural network model and Long Short-Term Memory (LSTM) neural network model. The results of empirical analysis show that the accuracy of BP-LSTM prediction model is significantly higher than that of traditional machine learning model, and it also has a great improvement compared with ordinary LSTM model.