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越来越多的学者投入到股市预测的研究中,探求股市发展规律,也不断有新的科学技术应用到股市预测,以求能够预先掌握其发展趋势。本文对各种股票预测方法研究进行综述。介绍了基于传统时间序列、隐马尔可夫模型等传统预测模型,以及基于机器学习、深度学习的决策树、神经网络、组合模型等新型创新模型,并介绍了各模型的优缺点,总结了基于神经网络的股票预测模型的基本步骤。最后,对股票预测研究方法做出总结与展望。
Abstract：More and more scholars have been involved in the research of stock market prediction, exploring the law of stock market development, and constantly applying new science and technology to stock market prediction, in order to be able to grasp its development trend in advance. This paper reviews the research on various stock forecasting methods. This paper introduces the traditional forecasting models based on traditional time series and hidden Markov model, as well as the new innovative models such as decision tree, neural network and combination model based on machine learning and deep learning. It also introduces the advantages and disadvantages of each model, and summarizes the basic steps of stock forecasting model based on neural network. Finally, the research methods of stock forecasting are summarized and prospected.
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基本信息:
DOI：10.16191/j.cnki.hbkx.2020.04.003
中图分类号:F832.51;TP181
引用信息:
[1]张倩倩,林天华,祁旭阳等.基于机器学习的股票预测研究综述[J].河北省科学院学报,2020,37(04):15-21.DOI:10.16191/j.cnki.hbkx.2020.04.003.
基金信息:
河北省自然科学基金项目(F2019207061)
