Financial time-series forecasting remains highly challenging due to non-stationarity, market noise, and complex dependencies across multiple temporal scales. Existing state-of-the-art models, although effective in long-sequence learning, often rely on single-stream architectures that struggle to disentangle heterogeneous temporal patterns and maintain contextual coherence. To address these limitations, this paper proposes HEDR-Net, a novel Heterogeneous Encoding Disentangled Representation Network that centres on a structural feature decoupling strategy and achieves coordinated modelling of trend, fluctuation, and raw signals. A wavelet-guided decomposition separates the input sequence into three semantically distinct channels, which are then encoded by structurally specialised subnetworks: Mamba for long-term trends, TCN for short-term fluctuations, and iTransformer for contextual dynamics. A dual cross-attention mechanism is introduced to enhance inter-branch interaction, followed by a Sparse Mixture of Feature Experts module that performs high-dimensional representation compression and adaptive fusion. Extensive experiments on multiple stock market benchmarks demonstrate that HEDR-Net consistently outperforms recent advanced models such as PatchTST and iTransformer, achieving superior forecasting accuracy, robustness, and cross-market generalisation. These results confirm the effectiveness of the proposed structural decoupling and heterogeneous fusion design in improving predictive performance and interpretability under complex financial conditions.
Bao et al. (Sun,) studied this question.