<![CDATA[Forecasting stock prices in the renewable energy sector remains a challenging task due to high market volatility, regulatory dynamics, and rapidly changing investor sentiment. Accurate prediction models are therefore essential to support informed investment decisions and reduce financial uncertainty. This study proposes a hybrid ensemble forecasting framework that integrates Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Support Vector Regression (SVR) to improve the prediction accuracy of renewable energy stock prices. The model leverages the complementary strengths of LSTM in capturing long-term temporal dependencies and GRU in efficiently modeling short-term market dynamics, while SVR acts as a meta-learner within a stacking ensemble architecture to enhance predictive generalization. To further strengthen the predictive capability, a set of technical indicators is incorporated to enrich the feature representation, and Bayesian optimization is employed to adaptively tune key hyperparameters. The model is evaluated using renewable energy stocks listed on the Indonesia Stock Exchange with hourly trading data, and its performance is assessed using multiple regression metrics, including Mean Squared Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Experimental results demonstrate that the proposed hybrid model consistently outperforms several benchmark approaches, achieving superior forecasting accuracy and more stable predictions across multiple stocks. These findings indicate that the integration of complementary recurrent architectures with ensemble learning and adaptive optimization provides a robust framework for modeling complex financial time-series data, offering improved predictive reliability for renewable energy stock forecasting.]]>
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