Research on Stock Price Prediction based on the GWO-LSTM-ATT Model
DOI:
https://doi.org/10.54097/6k81dw88Keywords:
Attention Mechanism, Grey Wolf Algorithm, Long Short-Term Memory Network, Stock Price Prediction, Time Series PredictionAbstract
To address the challenge of deep learning models becoming trapped in local optima and to enhance prediction accuracy in stock price forecasting, this study proposes a stock prediction model that utilizes the Grey Wolf Optimization (GWO) algorithm to optimize the Long Short-Term Memory with Attention Mechanism (LSTM-ATT). The GWO algorithm is employed to fine-tune the hyperparameters of the LSTM-ATT model, thereby identifying the optimal hyperparameter configuration necessary for constructing a high-accuracy stock prediction model. Experimental results indicate that for 5-step, 10-step, and 20-step predictions, the Mean Absolute Error (MAE) of the GWO-LSTM-ATT model is reduced by 61.33%, 77.06%, and 89.28%, respectively, in comparison to the LSTM-ATT model. The GWO-LSTM-ATT model not only effectively mitigates the issue of local optima but also offers a novel perspective and a robust tool for enhancing the accuracy and stability of stock price predictions.
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