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<p>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.</p>

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Multi-perspective Learning Based on Transformer for Stock Price Trend

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月27日

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  • Xiliang Li,
  • Shuoru Chen,
  • Xiaoyan Qiao,
  • Mingli Zhang,
  • Caiming Zhang,
  • Feng Zhao

摘要

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.