Abstract
Stock price prediction is a crucial task in quantitative trading. The recent advancements in deep learning have sparked interest in using neural networks to identify stock market patterns. However, existing deep learning models have limitations in exploring long dependencies in time-series data and capturing local features, making it challenging to reflect the impact of feature factors on stock prices. To address this, we propose a convolutional attention mechanism-based stock price prediction model, StPrformer. The model utilizes a convolutional attention mechanism to mine temporal dependencies between stock prices and feature factors. Additionally, the convolutional layer in the encoder provides direct a priori information of input features for prediction. Our experiments demonstrate that StPrformer outperforms existing deep learning models in terms of prediction accuracy. Compared to the classical Transformer prediction model, StPrformer reduces the average absolute error and mean square error by 33.3% and 26.1%, respectively. These results confirm the universality and superiority of StPrformer.
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Liu, Z., Zhang, Q., Huang, D., Wu, D. (2023). StPrformer: A Stock Price Prediction Model Based on Convolutional Attention Mechanism. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science(), vol 14090. Springer, Singapore. https://doi.org/10.1007/978-981-99-4761-4_37
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DOI: https://doi.org/10.1007/978-981-99-4761-4_37
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