MDN: A Multi-Scale Disentanglement Network for Time Series Forecasting

Although multi-scale modeling methods have achieved success in time series forecasting, they often overlook cross-scale entanglement and directly model cross-scale interactions among entangled features, allowing entanglement to interfere with interaction modeling. Extracting disentangled multi-scale features before modeling interactions can mitigate such interference. However, extracting such features faces two challenges: (1) Non-linear cross-scale entanglement is difficult to disentangle effectively. (2) Scale-shared information confounds the disentanglement process. To this end, we propose a **M**ulti-scale **D**isentanglement **N**etwork (MDN) for time series forecasting. Specifically, MDN first employs a Product-of-Experts-based separation module to infer and remove a robust scale-shared feature, thereby reducing interference from scale-shared information. Then, a mutual information (MI)-based disentanglement constraint is designed to minimize pairwise MI upper bounds among entangled features, thereby mitigating non-linear cross-scale entanglement. Finally, MDN sequentially models the intra-scale and inter-scale interactions among the disentangled features. Extensive experiments on 7 real-world datasets demonstrate that MDN achieves state-of-the-art performance against 14 competitive baselines. Code is available at: https://anonymous.4open.science/r/MDN-6BF2.