DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction
Abstract Financial management prediction, often known as financial forecasting, is the act of estimating future financial outcomes using past data and present trends. It is an essential component of financial analysis and planning that aids businesses in making well-informed decisions and preparing...
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| Format: | Article |
| Language: | English |
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Springer
2024-11-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01613-x |
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| author | Salem Knifo Ahmad Alzubi |
| author_facet | Salem Knifo Ahmad Alzubi |
| author_sort | Salem Knifo |
| collection | DOAJ |
| description | Abstract Financial management prediction, often known as financial forecasting, is the act of estimating future financial outcomes using past data and present trends. It is an essential component of financial analysis and planning that aids businesses in making well-informed decisions and preparing for potential future events. In the healthcare domain, financial management prediction is a crucial task that helps patients track and predict the expenses required for their medical services. The established methods for financial management prediction have some flaws, such as the requirement of labeled data, data quality, time complexity, under fitting problems, and longer execution times. Therefore, in order to resolve these limitations; a deep learning-based model is developed in this study for efficient financial management prediction. Specifically, this research proposes a dual-recurrent neural network with a tri-channel attention mechanism (DR-Z2AN) for accurate prediction. The proposed DR-Z2AN model combines the tri-channel attention mechanism with dual-RNN and multi-head attention, which enhances the robustness and interpretability of the systems. The multi-head attention learns the complex relationships between the data, which develops the generalization capability of the model in prediction tasks. The combined model efficiently processes the sequence data, and the tri-channel attention improves the model's capacity to extract meaningful characteristics from the input. The integration of the incentive learning approach helps the model improve the learning parameters to get better results with the minimum error. The experimental results demonstrate that the DR-Z2AN model attains minimal error in terms of MAE, MAPE, MSE, and RMSE of 1.46, 3.83, 4.32, and 2.08, respectively; thus, the proposed approach gives better results than the other traditional methods. Overall, the DR-Z2AN model offers accurate predictions with reduced computational time and improved interpretability. |
| format | Article |
| id | doaj-art-80e3fb77e34d4dc5992423b522a8f57b |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-80e3fb77e34d4dc5992423b522a8f57b2025-02-02T12:49:13ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-11-0111111610.1007/s40747-024-01613-xDR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management predictionSalem Knifo0Ahmad Alzubi1Institute of Social Sciences, University of Mediterranean KarpasiaInstitute of Social Sciences, University of Mediterranean KarpasiaAbstract Financial management prediction, often known as financial forecasting, is the act of estimating future financial outcomes using past data and present trends. It is an essential component of financial analysis and planning that aids businesses in making well-informed decisions and preparing for potential future events. In the healthcare domain, financial management prediction is a crucial task that helps patients track and predict the expenses required for their medical services. The established methods for financial management prediction have some flaws, such as the requirement of labeled data, data quality, time complexity, under fitting problems, and longer execution times. Therefore, in order to resolve these limitations; a deep learning-based model is developed in this study for efficient financial management prediction. Specifically, this research proposes a dual-recurrent neural network with a tri-channel attention mechanism (DR-Z2AN) for accurate prediction. The proposed DR-Z2AN model combines the tri-channel attention mechanism with dual-RNN and multi-head attention, which enhances the robustness and interpretability of the systems. The multi-head attention learns the complex relationships between the data, which develops the generalization capability of the model in prediction tasks. The combined model efficiently processes the sequence data, and the tri-channel attention improves the model's capacity to extract meaningful characteristics from the input. The integration of the incentive learning approach helps the model improve the learning parameters to get better results with the minimum error. The experimental results demonstrate that the DR-Z2AN model attains minimal error in terms of MAE, MAPE, MSE, and RMSE of 1.46, 3.83, 4.32, and 2.08, respectively; thus, the proposed approach gives better results than the other traditional methods. Overall, the DR-Z2AN model offers accurate predictions with reduced computational time and improved interpretability.https://doi.org/10.1007/s40747-024-01613-xFinancial management predictionDual-recurrent neural networkTri-channel attention mechanismMin–max scalar normalizationHealth care |
| spellingShingle | Salem Knifo Ahmad Alzubi DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction Complex & Intelligent Systems Financial management prediction Dual-recurrent neural network Tri-channel attention mechanism Min–max scalar normalization Health care |
| title | DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction |
| title_full | DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction |
| title_fullStr | DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction |
| title_full_unstemmed | DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction |
| title_short | DR-Z2AN: dual-recurrent neural network with a tri-channel attention mechanism for financial management prediction |
| title_sort | dr z2an dual recurrent neural network with a tri channel attention mechanism for financial management prediction |
| topic | Financial management prediction Dual-recurrent neural network Tri-channel attention mechanism Min–max scalar normalization Health care |
| url | https://doi.org/10.1007/s40747-024-01613-x |
| work_keys_str_mv | AT salemknifo drz2andualrecurrentneuralnetworkwithatrichannelattentionmechanismforfinancialmanagementprediction AT ahmadalzubi drz2andualrecurrentneuralnetworkwithatrichannelattentionmechanismforfinancialmanagementprediction |