BiLSTM-SDTCN-AutoCorr: A Hybrid Model for Stock Price Prediction Integrating Sequence Decomposition and Autocorrelation Attention - Fan, Weijun (International Journal of Computer Science and Information Technology, 2026)
Stock price prediction faces substantial challenges due to nonlinearity, non-stationarity, and noise contamination. Traditional econometric models and early Deep Learning methods struggle to effectively capture complex temporal patterns. This paper proposes a novel hybrid Neural Network, BiLSTM-SDTCN-AutoCorr, which refines a BiLSTM–Transformer backbone: a sequence decomposition module partitions the input series into trend and seasonal components to filter noise and enhance pattern separation; the vanilla self-attention mechanism is replaced by autocorrelation attention to efficiently capture periodic dependencies via the Fast Fourier transform; and the Transformer decoder is modified into Temporal Convolutional Network layers to strengthen local sequence modeling. The model is evaluated on five stock index datasets, and the results demonstrate significant superiority across evaluation metrics. The proposed model offers an efficient and robust solution for stock prediction with potential practical applicability.