AMDCnet: attention-gate-based multi-scale decomposition and collaboration network for long-term time series forecasting

Our research provides a robust baseline for the application of artificial intelligence in multivariate time series forecasting. This work leverages multi-scale time series decomposition and gated units for feature fusion, effectively capturing dependencies across different temporal scales. Future studies may further optimize the scale decomposition and fusion modules. Such efforts could enhance the representation of multi-scale information and help address key challenges in multivariate time series prediction.