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Stock prediction based on bidirectional gated recurrent unit with convolutional neural network and feature selection
- Source :
- PLoS ONE, PLoS ONE, Vol 17, Iss 2, p e0262501 (2022)
- Publication Year :
- 2022
- Publisher :
- Public Library of Science (PLoS), 2022.
-
Abstract
- With the development of recent years, the field of deep learning has made great progress. Compared with the traditional machine learning algorithm, deep learning can better find the rules in the data and achieve better fitting effect. In this paper, we propose a hybrid stock forecasting model based on Feature Selection, Convolutional Neural Network and Bidirectional Gated Recurrent Unit (FS-CNN-BGRU). Feature Selection (FS) can select the data with better performance for the results as the input data after data normalization. Convolutional Neural Network (CNN) is responsible for feature extraction. It can extract the local features of the data, pay attention to more local information, and reduce the amount of calculation. The Bidirectional Gated Recurrent Unit (BGRU) can process the data with time series, so that it can have better performance for the data with time series attributes. In the experiment, we used single CNN, LSTM and GRU models and mixed models CNN-LSTM, CNN-GRU and FS-CNN-BGRU (the model used in this manuscript). The results show that the performance of the hybrid model (FS-CNN-BGRU) is better than other single models, which has a certain reference value.
- Subjects :
- Computer and Information Sciences
China
Asia
Neural Networks
Stock Markets
Economics
Science
Social Sciences
Research and Analysis Methods
Machine Learning
Geographical Locations
Machine Learning Algorithms
Mathematical and Statistical Techniques
Deep Learning
Artificial Intelligence
Statistical Methods
Capital Markets
Financial Markets
Recurrent Neural Networks
Multidisciplinary
Applied Mathematics
Simulation and Modeling
Statistics
Biology and Life Sciences
Convolution
Physical Sciences
People and Places
Medicine
Neural Networks, Computer
Mathematical Functions
Mathematics
Algorithms
Research Article
Forecasting
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 17
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....1d3e52e44cdd1ccdf2781f204d87ab6e