计算机工程与应用2019，Vol.55Issue(11)：209-212,4.DOI:10.3778/j.issn.1002-8331.1811-0239
基于LSTM的股票价格预测建模与分析
Modeling and Analysis of Stock Price Forecast Based on LSTM
摘要
Abstract
Stock price volatility is a highly complex nonlinear system. The adjustment of stocks is not based on a uniform time process and has its own process of advancement. Combining the characteristics of LSTM(Long Short-Term Memory) recurrent neural network and the characteristics of stock market, and after preprocessing operations such as interpolation, wavelet noise reduction, and normalization of data, all of this data will be inputted into the LSTM network model of different LSTM layers and the number of different hidden neurons in the same layer for training and testing. Comparing the evaluation indicators with the prediction results, it finds the appropriate number of LSTM layers and hidden neurons, and improves the prediction accuracy by about 30% . The test results show that the computational complexity of this model is small and the prediction accuracy is improved. It not only provides a useful reference for predicting stock trend before stock investment, but also helps investors to build a suitable stock investment strategy after further understanding of the actual stock price.关键词
小波降噪/长短期记忆网络(LSTM)层数/隐藏神经元/股价预测Key words
wavelet noise reduction/number of Long Short-Term Memory(LSTM)layer/hidden neurons/stock price forecast分类
信息技术与安全科学引用本文复制引用
彭燕,刘宇红,张荣芬..基于LSTM的股票价格预测建模与分析[J].计算机工程与应用,2019,55(11):209-212,4.基金项目
贵州省科技计划项目(No.黔科合平台人才[2016]5707). （No.黔科合平台人才[2016]5707）
