A Comparative Study of CNN-sLSTM-Attention-Based Time Series Forecasting: Performance Evaluation on Data with Symmetry and Asymmetry Phenomena

This paper proposes a hierarchical CNN-sLSTM-Attention model for long-sequence time series forecasting. It enhances efficiency by replacing traditional LSTMs with a stable LSTM (sLSTM) variant, which incorporates exponential gating and memory mixing. The architecture integrates CNN for local feature extraction, sLSTM blocks for temporal modeling, and an attention mechanism for dynamic weighting. This integrated design enables the effective processing of data with symmetric patterns or asymmetric patterns, which are prevalent in real-world time series. Experimental results on six datasets—encompassing scenarios with symmetry/asymmetry characteristics, such as temperature cycles and traffic flow fluctuations—demonstrate the model’s superior performance. Key findings include a 33% reduction in RMSE over standard LSTM on temperature prediction; 10× faster convergence with stability achieved within 12 epochs for traffic flow prediction; a 15–47% reduction in long-sequence error attributable to the sLSTM component; and a 35% improvement in trend fitting due to the attention mechanism. Although the model outperforms baseline methods in handling periodic (often symmetric) and noisy data, its performance is limited on multimodal cases. These findings suggest that future work should focus on lightweight optimization, which has the potential to improve the model’s adaptability to a broader spectrum of symmetry and asymmetry patterns in time series.