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The Attention Mechanism is acknowledged as a machine learning method proficient in managing relationships within sequential data, surpassing traditional models in this regard. However, the unique characteristics of stock data, including substantial volatility, multidimensionality, and non-linear patterns, present challenges in attaining accurate forecasts of stock prices. This research aims to tackle these hurdles by enhancing a prior model through the incorporation of an Attention Mechanism, resulting in an enhanced model. The forecasted data are standardized and prepared for analysis before undergoing signal decomposition into high and low-frequency components. Subsequently, the Attention Mechanism processes the high-frequency signals. Evaluation entails comparing the performance of the proposed model with that of the previous model using identical parameters. The findings indicate that the proposed model achieves a reduced RMSE value of 0.5708777053 compared to the previous model's average RMSE value of 0.5823726212, indicating enhanced accuracy in stock price prediction. This approach is anticipated to make a substantial contribution to the advancement of more dependable and effective stock price prediction models, addressing the limitations of prior methodologies
Kelvin et al. (Fri,) studied this question.