Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer
It addresses the need for accurate stock price forecasting to guide investment and financial strategies, though it appears incremental as it combines existing techniques.
This study tackled stock price forecasting by proposing a composite framework integrating variational mode decomposition, PatchTST, and an adaptive scale-weighted layer, which demonstrated significant improvements in accuracy across four major stock indices from 2000 to 2024.
The significant fluctuations in stock index prices in recent years highlight the critical need for accurate forecasting to guide investment and financial strategies. This study introduces a novel composite forecasting framework that integrates variational mode decomposition (VMD), PatchTST, and adaptive scale-weighted layer (ASWL) to address these challenges. Utilizing datasets of four major stock indices--SP500, DJI, SSEC, and FTSE--from 2000 to 2024, the proposed method first decomposes the raw price series into intrinsic mode functions (IMFs) using VMD. Each IMF is then modeled with PatchTST to capture temporal patterns effectively. The ASWL module is applied to incorporate scale information, enhancing prediction accuracy. The final forecast is derived by aggregating predictions from all IMFs. The VMD-PatchTST-ASWL framework demonstrates significant improvements in forecasting accuracy compared to traditional models, showing robust performance across different indices. This innovative approach provides a powerful tool for stock index price forecasting, with potential applications in various financial analysis and investment decision-making contexts.