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基于粒子群优化LSTM的股票预测模型

宋刚 张云峰 包芳勋 秦超

宋刚, 张云峰, 包芳勋, 等 . 基于粒子群优化LSTM的股票预测模型[J]. 北京航空航天大学学报, 2019, 45(12): 2533-2542. doi: 10.13700/j.bh.1001-5965.2019.0388
引用本文: 宋刚, 张云峰, 包芳勋, 等 . 基于粒子群优化LSTM的股票预测模型[J]. 北京航空航天大学学报, 2019, 45(12): 2533-2542. doi: 10.13700/j.bh.1001-5965.2019.0388
SONG Gang, ZHANG Yunfeng, BAO Fangxun, et al. Stock prediction model based on particle swarm optimization LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2533-2542. doi: 10.13700/j.bh.1001-5965.2019.0388(in Chinese)
Citation: SONG Gang, ZHANG Yunfeng, BAO Fangxun, et al. Stock prediction model based on particle swarm optimization LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(12): 2533-2542. doi: 10.13700/j.bh.1001-5965.2019.0388(in Chinese)

基于粒子群优化LSTM的股票预测模型

doi: 10.13700/j.bh.1001-5965.2019.0388
基金项目: 

国家自然科学基金 61672018

国家自然科学基金 61772309

国家自然科学基金-浙江两化融合联合基金 U1609218

山东省重点研发计划 2016GSF120013

山东省重点研发计划 2017GGX10109

山东省重点研发计划 2018GGX101013

山东省高等学校优势学科人才队伍培育计划 

山东省自然科学杰出青年 ZR2018JL022

山东省自然科学基金 ZR2019MF051

详细信息
    作者简介:

    宋刚  男, 硕士研究生。主要研究方向:机器学习、数据分析

    张云峰   男, 博士, 教授, 博士生导师。主要研究方向:计算机视觉、数据分析

    通讯作者:

    张云峰, E-mail: yfzhang@sdufe.edu.cn

  • 中图分类号: TP183

Stock prediction model based on particle swarm optimization LSTM

Funds: 

National Natural Science Foundation of China 61672018

National Natural Science Foundation of China 61772309

National Natural Science Foundation of China-Zhejiang Two Integration Joint Fund U1609218

Key Research and Development Project of Shandong Province 2016GSF120013

Key Research and Development Project of Shandong Province 2017GGX10109

Key Research and Development Project of Shandong Province 2018GGX101013

Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions 

Natural Science Foundation for Excellent Youth of Shandong Province ZR2018JL022

Natural Science Foundation of Shandong Province ZR2019MF051

More Information
  • 摘要:

    为了提高股票时间序列预测精度,增强预测模型结构参数可解释性,提出一种基于自适应粒子群优化(PSO)的长短期记忆(LSTM)股票价格预测模型(PSO-LSTM),该模型在LSTM模型的基础上进行改进和优化,因此擅长处理具有长期依赖关系的、复杂的非线性问题。通过自适应学习策略的PSO算法对LSTM模型的关键参数进行寻优,使股票数据特征与网络拓扑结构相匹配,提高股票价格预测精度。实验分别以沪市、深市、港股股票数据构建了PSO-LSTM模型,并对该模型的预测结果与其他预测模型进行比较分析。结果表明,基于自适应PSO的LSTM股票价格预测模型不但提高了预测准确度,而且具有普遍适用性。

     

  • 图 1  LSTM单元结构

    Figure 1.  LSTM unit structure

    图 2  PSO-LSTM模型架构

    Figure 2.  PSO-LSTM model architecture

    图 3  上海浦发银行各模型预测结果比较

    Figure 3.  Comparison of prediction results of various models for Shanghai Pudong Development Bank

    图 4  LSTM与PSO-LSTM预测结果比较

    Figure 4.  Comparison of prediction results between LSTM and PSO-LSTM

    图 5  五粮液各模型预测结果比较

    Figure 5.  Comparison of prediction results of various models for Wuliangye

    图 6  恒隆集团各模型预测结果比较

    Figure 6.  Comparison of prediction results of various models for Hang Lung Group

    表  1  上海浦发银行各模型评价指标比较

    Table  1.   Comparison of various models' evaluation indicators for Shanghai Pudong Development Bank

    模型 RMSE MAPE/% MAE MSE R2
    ARIMA 0.1609 3.6176 0.3809 0.0259 0.9833
    SVM 0.1847 1.4149 0.1482 0.0341 0.9666
    MLP 0.1924 1.4665 0.1551 0.0374 0.9832
    RNN 0.2082 1.5145 0.1598 0.0433 1.3423
    LSTM 0.1799 1.3792 0.1451 0.0323 1.1518
    PSO-LSTM 0.1420 1.0369 0.1068 0.0202 1.0120
    下载: 导出CSV

    表  2  LSTM与PSO-LSTM评价指标比较

    Table  2.   Comparison of evaluation indicators between LSTM and PSO-LSTM

    模型 RMSE
    第1组 第2组 第3组 第4组 第5组
    LSTM 0.1072 0.1061 0.1442 0.1629 1.1534
    PSO-LSTM 0.1174 0.0913 0.1361 0.1240 0.1149
    下载: 导出CSV

    表  3  五粮液各模型评价指标比较

    Table  3.   Comparison of various models' evaluation indicators for Wuliangye

    模型 RMSE MAPE/% MAE MSE R2
    ARIMA 1.7814 11.1753 6.2734 3.2851 1.0178
    SVM 1.6896 2.4872 1.3764 2.8548 0.7726
    MLP 1.8892 2.6822 1.3879 3.4882 1.0605
    RNN 1.6549 2.3478 1.3247 2.7203 1.0344
    LSTM 1.6013 2.1247 1.1918 2.5734 1.0571
    PSO-LSTM 1.3427 1.7808 1.0342 1.8028 1.0124
    下载: 导出CSV

    表  4  恒隆集团各模型评价指标比较

    Table  4.   Comparison of various models' evaluation indicators for Hang Lung Group

    模型 RMSE MAPE/% MAE MSE R2
    ARIMA 0.3582 4.3628 0.8921 0.1282 1.2391
    SVM 0.5216 2.1204 0.4247 0.2721 0.5797
    MLP 0.3439 1.2296 0.2529 0.1183 0.9695
    RNN 0.3844 1.5902 0.3242 0.1478 0.9568
    LSTM 0.3649 1.4834 0.3026 0.1332 0.9567
    PSO-LSTM 0.2845 1.0472 0.2366 0.0812 1.0041
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-07-10
  • 录用日期:  2019-08-23
  • 刊出日期:  2019-12-20

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