| 基于LSTM神经网络的短期价格趋势预测 |
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| 引用本文： | 邓飞燕,岑少琪,钟凤琪,潘家辉. 基于LSTM神经网络的短期价格趋势预测[J]. 计算机系统应用, 2021, 30(4): 187-192. DOI: 10.15888/j.cnki.csa.007855 |
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| 作者姓名： | 邓飞燕 岑少琪 钟凤琪 潘家辉 |
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| 作者单位： | 华南师范大学 软件学院, 佛山 528225 |
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| 基金项目： | 广州市科技计划重点领域研发计划(202007030005); 广东省自然科学基金面上项目(2019A1515011375); 国家自然科学基金面上项目(61876067) |
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| 摘 要： | 本文主要对LSTM模型结构改进及优化其参数,使其预测股票涨跌走势准确率明显提高,同时对美股周数据及日数据在LSTM神经网络预测效果展开研究.一方面通过分析对比两者预测效果差别,验证不同数据集对预测效果的影响;另一方面为LSTM股票预测研究提供数据集的选择建议,以提高股票预测准确率.本研究通过改进后的LSTM神经网络模型...
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| 关 键 词： | 长短期记忆网络 股票价格预测 时间序列 短期价格 LSTM |
| 收稿时间： | 2020-08-02 |
| 修稿时间： | 2020-08-28 |
Short-Term Price Trend Forecast Based on LSTM Neural Network |
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| DENG Fei-Yan,CEN Shao-Qi,ZHONG Feng-Qi,PAN Jia-Hui. Short-Term Price Trend Forecast Based on LSTM Neural Network[J]. Computer Systems& Applications, 2021, 30(4): 187-192. DOI: 10.15888/j.cnki.csa.007855 |
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| Authors: | DENG Fei-Yan CEN Shao-Qi ZHONG Feng-Qi PAN Jia-Hui |
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| Affiliation: | School of Software, South ChinaNormalUniversity, Foshan 528225, China |
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| Abstract: | This study mainly optimizes the structure and parameters of the LSTM model, so that the accuracy of predicting the trend in stock prices is significantly improved. Besides, we investigate the weekly and daily data on US stock in terms of predicting the LSTM neural network. On one hand, we compare the difference to verify the impact of different data sets on the forecast. On the other hand, we provide selection suggestions for data sets so as to increase the accuracy of stock forecast. This study uses the multi-time-series stock forecast in the improved LSTM model to predict the trend in stock prices. The results demonstrate that the weekly data perform better in forecast than daily data. To be specific, the average accuracy of daily data and weekly data is 52.8% and 58%, respectively. In summary, the application of weekly data to training the LSTM model yields higher accuracy in stock forecast. |
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| Keywords: | Long Short-Term Memory (LSTM) stock price forecast time series short-term price LSTM |
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