Muticyclenet: Long-Term Time Series Forecasting Using Multi-Cycle and Residual Prediction

Long-term time series forecasting holds significant applications in various fields. Although existing deep-learning models have made great progress, they still fall short in dealing with complex periodic patterns and residual prediction. This paper proposes an improved model named MultiCycleNet, which enhances the performance of CycleNet by introducing multiperiod modeling and residual prediction mechanisms. Specifically, we design an explicit cycle extraction mechanism based on modular mapping and channel-specific learnable embeddings, enabling the model to learn and represent multiple periodicities. Furthermore, we propose a residual prediction scheme combining one-dimensional average pooling and multi-scale denoising. The theoretical motivation lies in separating low-frequency trends from high-frequency noise, ensuring stable residual learning. Extensive experiments on electricity, transportation, finance, and tobacco datasets demonstrate that MultiCycleNet achieves a better balance between prediction accuracy, robustness, and computational efficiency.