FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction
Time series forecasting in financial markets presents significant challenges due to the inherent nonlinearity, volatility, and dynamic nature of market data. The unique architecture of the flexible dendritic-driven gated recurrent network (FD-GRNet) enables it to effectively manage both long-term de...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10879477/ |
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| author | Tongyan Liu Jiayi Li Zihang Zhang Hang Yu Shangce Gao |
| author_facet | Tongyan Liu Jiayi Li Zihang Zhang Hang Yu Shangce Gao |
| author_sort | Tongyan Liu |
| collection | DOAJ |
| description | Time series forecasting in financial markets presents significant challenges due to the inherent nonlinearity, volatility, and dynamic nature of market data. The unique architecture of the flexible dendritic-driven gated recurrent network (FD-GRNet) enables it to effectively manage both long-term dependencies and nonlinear patterns in financial time series. In pursuit of this capability, FD-GRNet integrates two novel components: the flexible dendritic neuron model (FDNM), enhancing the model’s capacity to capture complex nonlinearities, and the dendritic gated recurrent network (DGRNet), improving its ability to handle temporal dependencies. Comprehensive experiments on major global stock market indices demonstrate that FD-GRNet consistently outperforms several comparative algorithms across multiple evaluation metrics. Ablation studies further highlight the essential roles of FDNM and DGRNet in improving the model’s accuracy and robustness. Future research will focus on optimizing the model for broader time series forecasting applications. |
| format | Article |
| id | doaj-art-8ddb220c1ebd4c8ababb4ed4f9dd8b9d |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8ddb220c1ebd4c8ababb4ed4f9dd8b9d2025-08-20T02:15:29ZengIEEEIEEE Access2169-35362025-01-0113282652827910.1109/ACCESS.2025.354107410879477FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market PredictionTongyan Liu0https://orcid.org/0009-0001-2775-3825Jiayi Li1https://orcid.org/0000-0001-9416-5575Zihang Zhang2https://orcid.org/0009-0008-3476-1728Hang Yu3Shangce Gao4https://orcid.org/0000-0001-5042-3261Faculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanFaculty of Engineering, University of Toyama, Toyama, JapanCollege of Computer Science and Technology, Taizhou University, Taizhou, ChinaFaculty of Engineering, University of Toyama, Toyama, JapanTime series forecasting in financial markets presents significant challenges due to the inherent nonlinearity, volatility, and dynamic nature of market data. The unique architecture of the flexible dendritic-driven gated recurrent network (FD-GRNet) enables it to effectively manage both long-term dependencies and nonlinear patterns in financial time series. In pursuit of this capability, FD-GRNet integrates two novel components: the flexible dendritic neuron model (FDNM), enhancing the model’s capacity to capture complex nonlinearities, and the dendritic gated recurrent network (DGRNet), improving its ability to handle temporal dependencies. Comprehensive experiments on major global stock market indices demonstrate that FD-GRNet consistently outperforms several comparative algorithms across multiple evaluation metrics. Ablation studies further highlight the essential roles of FDNM and DGRNet in improving the model’s accuracy and robustness. Future research will focus on optimizing the model for broader time series forecasting applications.https://ieeexplore.ieee.org/document/10879477/Flexible dendritic neuron modeldendritic gated recurrent networktime series forecastingstock market prediction |
| spellingShingle | Tongyan Liu Jiayi Li Zihang Zhang Hang Yu Shangce Gao FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction IEEE Access Flexible dendritic neuron model dendritic gated recurrent network time series forecasting stock market prediction |
| title | FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction |
| title_full | FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction |
| title_fullStr | FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction |
| title_full_unstemmed | FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction |
| title_short | FD-GRNet: A Dendritic-Driven GRU Framework for Advanced Stock Market Prediction |
| title_sort | fd grnet a dendritic driven gru framework for advanced stock market prediction |
| topic | Flexible dendritic neuron model dendritic gated recurrent network time series forecasting stock market prediction |
| url | https://ieeexplore.ieee.org/document/10879477/ |
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