Rethinking Attention Mechanism for Spatio-Temporal Modeling: A Decoupling Perspective in Traffic Flow Prediction

The attention mechanism has the advantage of handling long-term correlations, and has been widely adopted in multivariate time series (MTS) prediction.As an important application of MTS, traffic flow prediction has the most popular solution using transformerbased prediction models nowadays.Just with attention mechanism, those models can learn the spatio-temporal correlations from traffic data.However, the up-to-date linear prediction models have questioned the effectiveness of current transformer-based models in certain conditions, which provides new possibilities for more efficient work.We rethink the role of the attention mechanism during spatio-temporal modeling from a decoupling perspective, and propose DEC-Former for traffic flow prediction.Specifically, the trend and seasonal parts of the time series data, the geographical adjacency of the nodes in the road network, and the traditional encoderdecoder architecture, are respectively decoupled.Such decoupling leverages the attention mechanism's advantage to capture longterm and long-range correlations.From extensive experiments on four real-world datasets, our work proves better predictive performance and efficiency than state-of-the-art attention-based models.Two case studies further show the distinct real effects.