Anis, Sadaf M.Kabbya, Md Asif ShahidullahTalukder, Anika HasanChowdhury, Iffat JahanHossain, Muhammad Iqbal2026-09-302026-09-302024-01-01S. M. Anis, M. A. S. Kabbya, A. H. Talukder, I. J. Chowdhury and M. I. Hossain, "FAANG Stock Price Prediction : A hybrid approach integrating deep learning with ensemble learning," 2024 27th International Conference on Computer and Information Technology (ICCIT), Cox's Bazar, Bangladesh, 2024, pp. 2677-2682, doi: 10.1109/ICCIT64611.2024.11022386.97983315190942-s2.0-105009124058https://hdl.handle.net/10361/30311In this research, a novel hybrid approach has been proposed by implementing several well known deep learning models such as : Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Autoencoder, Multilayer Perceptron (MLP), and Recurrent Neural Network (RNN) and integrating the deep learning models with ensemble learning techniques such as - Stacking Ensemble, Voting ensemble to a unified framework to predict stock prices of major FAANG companies dataset. Proposed ensemble learning techniques give better performance and accuracy than individual deep learning models. The stacking ensemble uses a meta-learner to combine predictions from individual deep learning models, while on the other hand voting ensemble averages the predictions. Stacking ensemble demonstrates consistent performance across datasets from major FAANG companies outperforming individual models and reducing the variability observed in standalone predictions. The research also employs Advanced Evaluation Metrics such as : R2 score and Mean Squared Logarithmic Error (MSLE) , F1 - score to evaluate performance providing nuanced insights into predictive accuracy for time-series data with exponential trends. Unlike traditional methods that rely only on a single model our proposed hybrid approach leverages complementary strengths of multiple architectures ensuring robustness and improved accuracy across datasets. By integrating predictions through a meta learner the proposed method achieves consistent performance, outperforming individual models. The novelty of this research lies in the comprehensive integration of multiple deep learning models and regularization techniques which ensure enhanced generalization and overcoming the inherent challenges in time-series financial forecasting across various datasets.2677-2682en-USDeep learningRecurrent neural networksAccuracyStackingAutoencodersCompaniesPredictive modelsRobustnessEnsemble learningLong short term memoryStock price forecasting.Conference Proceeding10.1109/ICCIT64611.2024.11022386