Nonlinearity and high volatility of financial time series have made it difficult to predict stock price. However, thanks to recent developments in deep learning and methods such as long short-term memory (LSTM) and convolutional neural network (CNN) models, significant improvements have been obtained in the analysis of this type of data. Further, Empirical Mode Decomposition (EMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) algorithms decomposing time series to different frequency spectra are among the methods that could be effective in analyzing financial time series. Based on these theoretical frameworks, we propose novel hybrid algorithms, i.e., CEEMD-CNN-LSTM and EMD-CNN-LSTM, which could extract deep features and time sequences, which are finally applied to one-step-ahead prediction. The concept of the suggested algorithm is that when combining these models, some collaboration is established between them that could enhance the analytical power of the model. The practical findings confirm this claim and indicate that CNN alongside LSTM and CEEMD or EMD could enhance the prediction accuracy and outperform other counterparts. Further, the suggested algorithm with CEEMD provides better performance compared to EMD.

金融时间序列的非线性和高波动性使得难以预测股票价格。但是，由于深度学习的最新发展以及诸如长短期记忆（LSTM）和卷积神经网络（CNN）模型之类的方法，在分析此类数据方面已获得了显着改进。此外，将时间序列分解为不同频谱的经验模式分解（EMD）和完全集成经验模式分解（CEEMD）算法是可以有效分析财务时间序列的方法。基于这些理论框架，我们提出了新颖的混合算法，即CEEMD-CNN-LSTM和EMD-CNN-LSTM，可以提取深度特征和时间序列，最终应用于一步一步的预测。建议算法的概念是，当组合这些模型时，它们之间会建立一些协作，这可以增强模型的分析能力。实际发现证实了这一主张，并表明CNN与LSTM和CEEMD或EMD一起可以提高预测准确性，并且胜过其他同类产品。此外，与EMD相比，带有CEEMD的建议算法提供了更好的性能。