Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer

TL;DR

This paper presents a novel stock price forecasting framework combining variational mode decomposition, PatchTST, and adaptive scale-weighted layers, significantly improving prediction accuracy across major stock indices.

Contribution

The study introduces an integrated VMD-PatchTST-ASWL framework that effectively captures multi-scale temporal patterns for stock price forecasting, outperforming traditional models.

Findings

Enhanced forecasting accuracy across multiple stock indices

Robust performance demonstrated on datasets from 2000 to 2024

Effective decomposition and modeling of intrinsic market signals

Abstract

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.