Adaptive Model Integration for Stock Price Forecasting With CNN‐ATTN‐BiLSTM

ABSTRACT Stock price prediction is challenging due to the high volatility and complex nonlinear patterns in financial markets. Traditional time series forecasting methods often struggle to capture such intricate dynamics. To address this, we propose a novel adaptive model fusion framework that integrates Convolutional Neural Networks (CNN), Bidirectional Long Short‐Term Memory networks (BiLSTM), and Attention Mechanisms (ATTN) to improve stock price forecasting. Our approach employs multi‐scale feature extraction using multiple CNNs enhanced with attention to adaptively select relevant features across different time horizons. An adaptive fusion mechanism dynamically adjusts the contribution of each sub‐model according to input data, optimizing predictions under varying market conditions. We investigated the effects of both univariate and multivariate data on model performance, and analyze how data distribution characteristics influence forecasting accuracy. Experiments were conducted on stock data from the top 9 companies in the Nasdaq 100 index by market capitalization, validating the robustness and effectiveness of our method across different sectors. Results show that our model significantly outperformed traditional forecasting approaches, achieving higher accuracy and improved generalization in diverse market environments. This study offers a novel framework for stock price prediction and provides valuable insights into adaptive model integration for financial time series forecasting.