Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866913485584072704 |
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| author | Xue, Xiaorui Li, Shaofang Wang, Xiaonan |
| author_facet | Xue, Xiaorui Li, Shaofang Wang, Xiaonan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16707 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer Xue, Xiaorui Li, Shaofang Wang, Xiaonan Machine Learning Signal Processing 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. |
| title | Enhanced forecasting of stock prices based on variational mode decomposition, PatchTST, and adaptive scale-weighted layer |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2408.16707 |