Stock price prediction using deep learning and frequency decomposition
By Hadi Rezaei, Hamidreza Faaljou, Gholamreza Mansourfar. Expert systems with applications, 2020
323 citations recorded by the source corpus.
Abstract 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.
Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Signal Processing
View Paper: https://www.semanticscholar.org/paper/fc7067e6e1c2892ec088adba64ba1442ce48ea43
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Electrical and Electronic Engineering
In Management Science and Operations Research
In Signal Processing
Citations in the corpus
- Stock price prediction with optimized deep LSTM network with artificial rabbits optimization algorithm (2023, methodology): “Rezaei et al. (2021) presented hybrid algorithms, CEEMD-CNN-LSTM and EMD-CNN-LSTM, which employ full ensemble empirical mode decomposition (CEEMD) and empirical mode decomposition (EMD) to extract deep features and time sequences for one-step-ahead stock price prediction.”