Time series forecasting is essential in many fields, such as financial analysis, climate forecasting, and energy demand. This study proposes an improved dilated convolutional(DC) AutoConNet architecture to solve the problem of accurate long-term forecasting of complex time series data. The model significantly improves its robustness and generalization ability by integrating multi-scale dilated convolution, layer normalization, and a new adaptive rescaling mechanism(ARM). The main improvement is that while maintaining the efficiency of the original AutoConNet, it effectively solves the overfitting problem and the defect of insufficient capture of temporal dependencies. We evaluated the model performance on 16 standard datasets (including finance, climate, health, etc.), such as M4, M5, ETTh1, ETTh2, ETTm1, and ETTm2. The ARM-DC AutoConNet achieved significant improvements on multiple datasets, especially in the long forecast period, which can significantly reduce the SMAPE index and stabilize the shape value. Comparative experiments demonstrate that the proposed model consistently outperforms or equals the benchmark AutoConNet in terms of error indicators. Furthermore, the proposed model surpasses the AutoConNet Model in error metrics. The most significant improvement is the combination of the adaptive rescaling mechanism and dilated convolution. The improved convolutional architecture provides a feasible solution for reliable long-term prediction and inspires the future development of time series deep learning.
Li et al. (Mon,) studied this question.