Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network
Stock prices forecasting is a topic research in the fields of investment and national policy, which has been a challenging problem owing to the multi-noise, nonlinearity, high-frequency, and chaos of stocks. These characteristics of stocks impede most forecasting models from extracting valuable info...
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doaj-a923b65edfb9454ca4bf3405292ada062021-03-30T02:34:40ZengIEEEIEEE Access2169-35362020-01-01811736511737610.1109/ACCESS.2020.30042849122554Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural NetworkQian Chen0Wenyu Zhang1https://orcid.org/0000-0002-8906-5411Yu Lou2School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, ChinaSchool of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, ChinaSchool of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, ChinaStock prices forecasting is a topic research in the fields of investment and national policy, which has been a challenging problem owing to the multi-noise, nonlinearity, high-frequency, and chaos of stocks. These characteristics of stocks impede most forecasting models from extracting valuable information from stocks data. Herein, a novel hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network is proposed. First, the raw data including four types of datasets (historical prices of stocks, technical indicators of stocks closing prices, natural resources prices, and historical data of the Google index) are transformed into a knowledge base with reduced dimensions using principal component analysis. Subsequently, multi-layer perceptron is used for the fast transformation of feature space and rapid gradient descent, bidirectional long-short term memory neural network for extracting temporal features of stock time series data, and attention mechanism for making the neural network focus more on crucial temporal information by assigning higher weights. Finally, a comprehensive model evaluation method is used to compare the proposed model with seven related baseline models. After extensive experiments, the proposed model demonstrated its good forecasting performance.https://ieeexplore.ieee.org/document/9122554/Stock prices forecastingattention mechanismbidirectional long-short term memory neural networkmulti-layer perceptrondeep learning |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Qian Chen Wenyu Zhang Yu Lou |
spellingShingle |
Qian Chen Wenyu Zhang Yu Lou Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network IEEE Access Stock prices forecasting attention mechanism bidirectional long-short term memory neural network multi-layer perceptron deep learning |
author_facet |
Qian Chen Wenyu Zhang Yu Lou |
author_sort |
Qian Chen |
title |
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network |
title_short |
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network |
title_full |
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network |
title_fullStr |
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network |
title_full_unstemmed |
Forecasting Stock Prices Using a Hybrid Deep Learning Model Integrating Attention Mechanism, Multi-Layer Perceptron, and Bidirectional Long-Short Term Memory Neural Network |
title_sort |
forecasting stock prices using a hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
Stock prices forecasting is a topic research in the fields of investment and national policy, which has been a challenging problem owing to the multi-noise, nonlinearity, high-frequency, and chaos of stocks. These characteristics of stocks impede most forecasting models from extracting valuable information from stocks data. Herein, a novel hybrid deep learning model integrating attention mechanism, multi-layer perceptron, and bidirectional long-short term memory neural network is proposed. First, the raw data including four types of datasets (historical prices of stocks, technical indicators of stocks closing prices, natural resources prices, and historical data of the Google index) are transformed into a knowledge base with reduced dimensions using principal component analysis. Subsequently, multi-layer perceptron is used for the fast transformation of feature space and rapid gradient descent, bidirectional long-short term memory neural network for extracting temporal features of stock time series data, and attention mechanism for making the neural network focus more on crucial temporal information by assigning higher weights. Finally, a comprehensive model evaluation method is used to compare the proposed model with seven related baseline models. After extensive experiments, the proposed model demonstrated its good forecasting performance. |
topic |
Stock prices forecasting attention mechanism bidirectional long-short term memory neural network multi-layer perceptron deep learning |
url |
https://ieeexplore.ieee.org/document/9122554/ |
work_keys_str_mv |
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