Multi-submodel fusing attention mechanism for time series prediction

Time series data usually have complex dynamic characteristics, and it is difficult for a single prediction model to fully capture the various patterns contained. To alleviate the issue, different models obtaining different features, we propose a novel mode called MSMA having multi-submodel to capture complex dynamics and employ the attention mechanism to fit the weights. Experimental results indicate that the proposed MSMA surpasses the state-of the art model in terms of performance for time series prediction.