Thach Tien Thanh
Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City 700000, Vietnam.
Entropy (Basel). 2025 Jan 17;27(1):82. doi: 10.3390/e27010082.
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.
准确预测股票市场指数对投资者、金融分析师和政策制定者至关重要。编码器和解码器架构的整合，再加上注意力机制，已成为提高预测准确性的一种强大方法。本文提出了一个新颖的框架，该框架利用这些组件来捕捉股价数据中的复杂时间依赖性和模式。编码器有效地将输入序列转换为密集表示，然后解码器使用该表示来重建未来值。注意力机制提供了额外的复杂性，使模型能够有选择地关注输入序列的相关部分以进行预测。此外，采用贝叶斯优化来微调超参数，进一步提高预测精度。我们的结果表明，与传统循环神经网络相比，预测精度有显著提高。这表明我们的集成方法有潜力有效处理股价数据中的复杂模式和依赖性。
