Hybrid Machine Learning Architecture for Stock Market Price Prediction: Integrating Statistical Time-Series Models with Deep Learning and Ensemble Methods
Copyright policy )Hybrid Machine Learning Architecture for Stock Market Price Prediction: Integrating Statistical Time-Series Models with Deep Learning and Ensemble Methods
Stock prediction is hard. Prices are noisy, nonstationary, and nonlinear. We built a hybrid system that combines statistical models (ARIMA, GARCH), deep learning (LSTM, GRU), and Random Forests via Ridge regression meta-learning. The meta-learner uses 5-fold time-series cross-validation to adaptively weight models. Testing across 20 stocks from Technology, Finance, Healthcare, Consumer, and Industrial sectors, we achieved 87.74 percent average RMSE improvement over individual models. Directional accuracy ranged from 42.45 percent to 85.87 percent. Boeing (BA) showed 95.43 percent RMSE improvement with 85.87 percent directional accuracy, U.S. Bancorp (USB) hit 94.31 percent RMSE improvement. Random Forest dominated the learned weights (60-92 percent), while ARIMA and deep learning added complementary signals. Walk-forward validation with 252-day rolling windows ensured that we tested on truly unseen data, not on retrofitted history.
Stock Price Prediction, GARCH, Random Forest, Hybrid Machine Learning, GRU, Meta-Learning, Time-Series Forecasting, ARIMA, LSTM, Ensemble Learning
Stock Price Prediction, GARCH, Random Forest, Hybrid Machine Learning, GRU, Meta-Learning, Time-Series Forecasting, ARIMA, LSTM, Ensemble Learning
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