Selective Enhanced Attention and Cross-Component Interaction for Long-Term Time Series Forecasting

Transformer-based methods have demonstrated promising performance in long-term time series forecasting. However, existing methods often consider interactions among all variables when modeling cross-variable correlations, which may introduce noise and redundant features from unrelated variables. These features distract the model's attention from relevant ones and increase the risk of overfitting. Moreover, the intertwined nature of long-term trends and short-term fluctuations in time series makes it challenging to model temporal dependencies effectively. To address these challenges, we propose the Selective Enhanced Time Series Transformer (SETST), which incorporates two key modules: the Selective Enhanced Attention (SEA) module and the Decomposition Component Fusion (DCF) module. SEA selectively captures correlations between highly related variables while filtering out meaningless information, facilitating cross-variable interaction learning. DCF integrates time series decomposition with cross-component interaction, enabling effective modeling of temporal dependencies. SETST demonstrates state-of-the-art performance on five real-world datasets.