Adaptive Decomposition and Dual-Branch Linear Forecasting for Stock Price Prediction

Source abstract

Stock price forecasting is important for financial decision-making but remains challenging because of complex and volatile market dynamics. Although existing forecasting methods have achieved considerable progress, they still face several limitations. Decomposition-based approaches are sensitive to manually specified parameters, deep learning models often require high computational resources, and individual lightweight models may not adequately capture diverse temporal characteristics. To address these limitations, this study proposes an improved whale optimization-based variational mode decomposition and dual-branch normalized linear forecasting (IWVMD-DNFLinear) framework, which integrates adaptive sequence decomposition, complementary linear forecasting, and learnable fusion. An improved beluga whale optimization (IBWO) algorithm is employed to adaptively optimize the parameters of variational mode decomposition (VMD). The decomposed components are then processed by parallel decomposition-based linear forecasting (DLinear) and normalized linear forecasting (NLinear) branches with a learnable fusion mechanism. Extensive experiments on four stock datasets under multiple forecasting horizons demonstrate that IWVMD-DNFLinear achieves consistently competitive forecasting performance against the trained baseline models across different datasets and forecasting horizons. On the AAPL dataset under single-step forecasting, IWVMD-DNFLinear achieves the lowest root mean square error (RMSE) of 5.1220. The DNFLinear forecasting network contains only 743 trainable parameters and requires 0.0110 MB of model storage. The proposed framework achieves high forecasting accuracy while maintaining a parameter-efficient forecasting structure, providing a practical solution for stock price forecasting.