Multi-perspective Learning Based on Transformer for Stock Price Trend
Abstract Stock constitutes a crucial element of the financial market, and accurately forecasting stock trends remains a significant and unresolved issue. Nonetheless, the stock’s considerable complexity renders accurate prediction of stock trends more challenging. This paper proposes a novel multi-p...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-02-01
|
| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00768-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract Stock constitutes a crucial element of the financial market, and accurately forecasting stock trends remains a significant and unresolved issue. Nonetheless, the stock’s considerable complexity renders accurate prediction of stock trends more challenging. This paper proposes a novel multi-perspective approach that converts the time series prediction challenge into an image classification problem, referred to as the Multi-perspective Denoise Transformer (MPDTransformer). We initially multi-factor features into two-dimensional images employing a multi-perspective approach to more comprehensively explain the actual market conditions and enhance the model’s practicality and adaptability; secondly, we utilize a Convolutional Autoencoder (CAE) to extract features, which effectively eliminates noise and enhances data purity; finally, to comprehensively capture the temporal relationships within the data and gain a deeper understanding of the overall time series, we employ a Transformer for prediction. Experimental results demonstrate that our method outperforms other prevalent stock trend prediction techniques. |
|---|---|
| ISSN: | 1875-6883 |