RCSAN residual enhanced channel spatial attention network for stock price forecasting Article Swipe

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Wenjie Sun , Ziyang Liu , C. Z. Yuan , Xiang Zhou , Yuting Pei , Wei Cui ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.1038/s41598-025-06885-y

This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. The R-CSAN adopts an encoder-decoder architecture, where the encoder extracts feature correlations from historical data through multiple layers of channel-spatial attention modules, and the decoder incorporates a masking mechanism to prevent future information leakage and introduces a cross-attention mechanism to model inter-market correlations. Experiments conducted on four cross-market stock datasets, including Amazon, Maotai, Ping An, and Vanke, demonstrate that R-CSAN significantly outperforms not only traditional baseline models such as ARIMA, LSTM, and CNN-LSTM, but also recent Transformer-based approaches like Informer, Autoformer, and iTransformer on metrics including RMSE, MAE, MAPE, [Formula: see text], and return on investment. The model reduces RMSE by 17.3-49.3% compared to traditional methods and 6.2-11.6% compared to Transformer variants, with the highest [Formula: see text] reaching 93.17% and an increase in return on investment to 482.64%. Ablation experiments confirm the critical contributions of each component, with the temporal module removal causing an average increase of 38.6% in RMSE and channel-spatial attention removal resulting in a 21.3% increase. Moreover, the model provides an interpretative analysis of features and temporal dimensions through attention weight visualization, offering insights into both indicator importance and critical time periods for prediction. In practical applications, R-CSAN's outputs can be integrated into quantitative trading strategies including breakout trading, moving average crossover signals, and portfolio allocation optimization, providing a new paradigm for robust prediction in highly volatile markets.

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Residual Stock (firearms) Stock price Computer science Econometrics Economics Geography Algorithm Biology Archaeology Paleontology Series (stratigraphy)

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Type: article
Language: en
Landing Page: https://doi.org/10.1038/s41598-025-06885-y
PDF: https://www.nature.com/articles/s41598-025-06885-y.pdf
OA Status: gold
Cited By: 8
References: 49
Related Works: 10
OpenAlex ID: https://openalex.org/W4411847541

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DOI: https://doi.org/10.1038/s41598-025-06885-y

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Title: RCSAN residual enhanced channel spatial attention network for stock price forecasting

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Language: en

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Publication year: 2025

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Publication date: 2025-07-01

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Authors: Wenjie Sun, Ziyang Liu, C. Z. Yuan, Xiang Zhou, Yuting Pei, Wei Cui

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Landing page: https://doi.org/10.1038/s41598-025-06885-y

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Concepts: Residual, Stock (firearms), Stock price, Computer science, Econometrics, Economics, Geography, Algorithm, Biology, Archaeology, Paleontology, Series (stratigraphy)

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Cited by: 8

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