A dual-path convolutional neural network combined with an attention-based bidirectional long short-term memory network for stock price prediction

The complexities of stock price data, characterized by its nonlinearity, non-stationarity, and intricate spatiotemporal patterns, make accurate prediction a substantial challenge. To address this, we propose the DCA-BiLSTM model, which combines dual-path convolutional neural networks with an attention mechanism (DCA) and bidirectional long short-term memory networks (BiLSTM). This model captures deep information and complex dependencies within time-series data. First, wavelet packet decomposition extracts high- and low-frequency features, followed by DCA for robust deep feature extraction, and finally, BiLSTM models bidirectional dependencies.股票价格数据兼具非线性、非平稳性与复杂时空模式等固有特性，其内在复杂性使得精准预测成为一项极具挑战性的任务。为应对这一难题，我们提出了DCA-BiLSTM模型，该模型将双路径卷积神经网络结合注意力机制（Dual-path Convolutional Neural Network with Attention Mechanism，DCA）与双向长短期记忆网络（Bidirectional Long Short-Term Memory Network，BiLSTM）进行融合。该模型能够有效捕捉时序数据中的深层信息与复杂依赖关系。具体而言，首先通过小波包分解提取数据的高低频特征，随后利用DCA进行鲁棒的深层特征提取，最终借助BiLSTM建模时序数据的双向依赖关系。