Distillation enhanced time series forecasting network with momentum contrastive learning

Haozhi Gao, Qianqian Ren, Jinbao Li

Abstract: Highlights•Proposing a distillation enhanced framework for time series forecasting.•Solving distribution shift problem and noise false positive focusing of contrastive learning method.•Adopting teacher-student paradigm for contrastive learning to enhance model ability.•Experiments on real-world datasets show the superiority of the proposed model.