Long Short-Term Financial Time Series Forecasting Based on Residual Multiscale TCN Sparse Expert Network and Informer

Due to the inherent high volatility and complexity of financial markets, traditional time series forecasting models face numerous challenges in handling both short- and long-term predictions in the stock market. Most traditional neural network-based financial prediction models are limited to short-term forecasting and struggle to capture long-term trends and global dependencies in the market fully. To address this, we propose a novel network architecture called ResMMoT-Informer. This model combines the strengths of the residual multiscale temporal convolutional network (TCN) sparse expert network (ResMMoT) and the Informer, enabling it to effectively capture multiscale local features and global dependencies in the stock market. ResMMoT achieves stable training through a residual structure and a sparse multiscale TCN expert network, allowing it to flexibly model complex temporal features and learn trends across different time-step scales. Meanwhile, the Informer optimizes long-sequence forecasting performance through an improved self-attention mechanism. Additionally, we introduce the wavelet noise reduction (WNR) method, further enhancing the model's robustness and prediction accuracy. In the experimental section, ablation experiments first validate the effectiveness and necessity of the proposed strategies and network structure. Subsequent comparison experiments on the NASDAQ100 dataset demonstrate that ResMMoT-Informer excels in both long- and short-term time series forecasting tasks in the stock market, with significantly better prediction accuracy and generalization ability than existing models. Compared to other popular neural network-based financial forecasting models, ResMMoT-Informer leads in prediction accuracy, time robustness, and interpretability, showcasing its cutting-edge advantage in contemporary research.