股票市场
时间序列
计算机科学
人工智能
系列(地层学)
股市预测
机器学习
深度学习
计量经济学
经济
地理
古生物学
背景(考古学)
考古
生物
作者
Diaa Salama Abd Elminaam,Asmaa M M. El-Tanany,Mohamed Abd El Fattah,Mustafa Abdul Salam
出处
期刊:International Journal of Advanced Computer Science and Applications
[The Science and Information Organization]
日期:2024-01-01
卷期号:15 (4)
标识
DOI:10.14569/ijacsa.2024.0150446
摘要
The article introduces a novel approach for forecasting stock market prices, employing a computationally efficient Bidirectional Long Short-Term Memory (BiLSTM) model enhanced with a global pooling mechanism. Based on the deep learning framework, this method leverages the temporal dynamics of stock data in both forward and reverse time frames, enabling enhanced predictive accuracy. Utilizing datasets from significant market players—HPQ, Bank of New York Mellon, and Pfizer—the authors demonstrate that the proposed single-layered BiLSTM model, optimized with RMSprop, significantly outperforms traditional Vanilla and Stacked LSTM models. The results are quantitatively evaluated using root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R^2), where the BiLSTM model shows a consistent improvement in all metrics across different stock datasets. We optimized the hyperparameters tuning using two distinct optimizers (ADAM, RMSprop) on the HPQ, New York Bank, and Pfizer datasets. The dataset has been preprocessed to account for missing values, standardize the features, and separate it into training and testing sets. Moreover, line graphs and candlestick charts illustrate the models' ability to capture stock market trends. The proposed algorithms attained respective RMSE values of 0.413, 0.704, and 0.478. the proposed algorithms attained respective RMSE values of 0.413, 0.704, and 0.478. The results show the proposed methods' superiority over recently published models. In addition, it is concluded that the proposed single-layered BiLSTM-based architecture is computationally efficient and can be recommended for real-time applications involving Stock market time series data.
科研通智能强力驱动
Strongly Powered by AbleSci AI