| Popis: |
Financial market prediction has shown considerable potential in the past few years from the combination of contemporary Deep Learning (DL) techniques and traditional time series forecasting methodologies. To predict the stock prices of three distinct companies General Electric (GE), Microsoft (MSFT), and Amazon (AMZN) datasets. This study presents a novel hybrid model that combines the Double Exponential Smoothing (DES) method with a Deep Learning (DL) model Dual Attention Encoder-Decoder based Bi-directional GRU, optimized using Bayesian Optimization (DES-DA-ED-Bi-GRU-BO). By combining the best features of contemporary and old methods, the hybrid model seeks to efficiently identify patterns and trends in stock data. When handling time series data, the DES method offers a reliable and flexible mechanism that considers trends and seasonality in the data. The DA-ED-Bi-GRU added to the deep learning model further improves its comprehension of intricate patterns found in the stock data. The parameters are adjusted using Bayesian optimization (BO) to maximize the model’s performance. Several performance indicators, such as Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-Square ( $R^{2}$ ), and Theil’s U-Statistics (TUS), are used to assess the effectiveness of the model. These measures offer thorough insights into the precision, dependability, and accuracy of the model’s predictions. The experimental findings show that the proposed hybrid model has the ability to predict GE, MSFT, and AMZN stock values with reasonable accuracy. Along with the optimization framework, DL and conventional smoothing approaches combine to provide a potent forecasting tool that may help traders and investors make wise judgments. |
