Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism
Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel frame...
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MDPI AG
2025-01-01
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| Series: | Entropy |
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| Online Access: | https://www.mdpi.com/1099-4300/27/1/82 |
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| author | Tien Thanh Thach |
| author_facet | Tien Thanh Thach |
| author_sort | Tien Thanh Thach |
| collection | DOAJ |
| description | Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages these components to capture complex temporal dependencies and patterns within stock price data. The encoder effectively transforms an input sequence into a dense representation, which the decoder then uses to reconstruct future values. The attention mechanism provides an additional layer of sophistication, allowing the model to selectively focus on relevant parts of the input sequence for making predictions. Furthermore, Bayesian optimization is employed to fine-tune hyperparameters, further improving forecast precision. Our results demonstrate a significant improvement in forecast precision over traditional recurrent neural networks. This indicates the potential of our integrated approach to effectively handle the complex patterns and dependencies in stock price data. |
| format | Article |
| id | doaj-art-b9f1cd3226c6439e86364e664bd6b177 |
| institution | Kabale University |
| issn | 1099-4300 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj-art-b9f1cd3226c6439e86364e664bd6b1772025-01-24T13:31:56ZengMDPI AGEntropy1099-43002025-01-012718210.3390/e27010082Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention MechanismTien Thanh Thach0Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 700000, VietnamAccurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration of encoder and decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents a novel framework that leverages these components to capture complex temporal dependencies and patterns within stock price data. The encoder effectively transforms an input sequence into a dense representation, which the decoder then uses to reconstruct future values. The attention mechanism provides an additional layer of sophistication, allowing the model to selectively focus on relevant parts of the input sequence for making predictions. Furthermore, Bayesian optimization is employed to fine-tune hyperparameters, further improving forecast precision. Our results demonstrate a significant improvement in forecast precision over traditional recurrent neural networks. This indicates the potential of our integrated approach to effectively handle the complex patterns and dependencies in stock price data.https://www.mdpi.com/1099-4300/27/1/82recurrent neural networkslong short-term memorygated recurrent unitsencoder–decoder architectureattention mechanismBayesian optimization |
| spellingShingle | Tien Thanh Thach Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism Entropy recurrent neural networks long short-term memory gated recurrent units encoder–decoder architecture attention mechanism Bayesian optimization |
| title | Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism |
| title_full | Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism |
| title_fullStr | Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism |
| title_full_unstemmed | Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism |
| title_short | Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism |
| title_sort | forecasting stock market indices using integration of encoder decoder and attention mechanism |
| topic | recurrent neural networks long short-term memory gated recurrent units encoder–decoder architecture attention mechanism Bayesian optimization |
| url | https://www.mdpi.com/1099-4300/27/1/82 |
| work_keys_str_mv | AT tienthanhthach forecastingstockmarketindicesusingintegrationofencoderdecoderandattentionmechanism |