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<p>Financial markets are highly non-linear and noisy, which complicates stock price forecasting. Traditional machine learning models often fail to appropriately generalize noisy data. Additionally, incorporating redundant attributes increases training complexity and overfitting risk. In this study, a hybrid model is proposed that combines noise reduction and feature selection techniques to improve forecasting accuracy and efficiency. Specifically, we propose a fusion noise identification and attribute reduction method based on independent component analysis (ICA), signal-to-noise ratio (SNR) theory, and neighborhood rough set (NRS) theory to address the aforementioned problems. First, technical indicators are established and used as alternative inputs for ICA. The aim is to determine the optimal ICA input dimension and noise sequence by maximizing the SNR while minimizing the reconstructed similarity of the series. Second, we calculate the attribute significance using the neighborhood rough set and obtain an attribute subset that satisfies the threshold filter condition. Finally, an attention mechanism-based (AM) long short-term memory (LSTM) neural network is used to build a forecasting model for the noise-reduced close price and reduced set of attributes. The research findings indicate that for time series denoising based on ICA, optimal results are achieved with an appropriate input dimension. Excessive input sequences introduce noise or redundant information. Attribute reduction based on NRS effectively reduces both temporal and spatial computational complexity without sacrificing generalization accuracy.</p>

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Hybrid stock price forecasting model using LSTM of attention mechanism combining noise removal and neighborhood rough set

  • 期刊论文

  • 发表日期：

    2026年1月21日

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  • Yuqi Guo,
  • Bingzhen Sun,
  • Juncheng Bai,
  • Weiping Ding

摘要

Financial markets are highly non-linear and noisy, which complicates stock price forecasting. Traditional machine learning models often fail to appropriately generalize noisy data. Additionally, incorporating redundant attributes increases training complexity and overfitting risk. In this study, a hybrid model is proposed that combines noise reduction and feature selection techniques to improve forecasting accuracy and efficiency. Specifically, we propose a fusion noise identification and attribute reduction method based on independent component analysis (ICA), signal-to-noise ratio (SNR) theory, and neighborhood rough set (NRS) theory to address the aforementioned problems. First, technical indicators are established and used as alternative inputs for ICA. The aim is to determine the optimal ICA input dimension and noise sequence by maximizing the SNR while minimizing the reconstructed similarity of the series. Second, we calculate the attribute significance using the neighborhood rough set and obtain an attribute subset that satisfies the threshold filter condition. Finally, an attention mechanism-based (AM) long short-term memory (LSTM) neural network is used to build a forecasting model for the noise-reduced close price and reduced set of attributes. The research findings indicate that for time series denoising based on ICA, optimal results are achieved with an appropriate input dimension. Excessive input sequences introduce noise or redundant information. Attribute reduction based on NRS effectively reduces both temporal and spatial computational complexity without sacrificing generalization accuracy.