RCSAN residual enhanced channel spatial attention network for stock price forecasting

Ziyang Liu · Chelsea Yuan · Xiang Zhou

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Stock Market Forecasting Methods Forecasting Techniques and Applications Time Series Analysis and Forecasting

Abstract

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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References

2017Deep learning with long short-term memory networks for financial market predictions 2020Empirical Asset Pricing via Machine Learning n.d.Multivariate GARCH models: a survey 2017A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction 2014Predicting stock and stock price index movement using Trend Deterministic Data Preparation and machine learning techniques 2017Stock price prediction using LSTM, RNN and CNN-sliding window model 2017Stock market's price movement prediction with LSTM neural networks 2016Deep Learning for Finance: Deep Portfolios

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