Abstract: Nowadays,the prediction of long sequence time series problems is mainly based on RNN like models,and most of the loss functions used are mean square error(MSE).However,MSE loss function can not capture the nonlinear problems commonly existing in long time series data.Moreover,MSE loss function itself is sensitive to outliers and has low robustness.Therefore,this paper proposes to use the improved Kernel MSE loss function based on kernel technique to replace the traditional MSE loss function in Informer model,and solve the nonlinearity in data by mapping the error from the original feature space to a higher dimensional space.Moreover,the first and second derivatives of the new loss function ensure robustness to outliers.Under the background of multivariable prediction and multivariable,this paper compares the prediction accuracy with the classical Informer model,LSTM model and GRU model,taking eight data sets in three types of data as examples.The results show that the improved Informer model has higher prediction accuracy,and the relative improvement value of accuracy increases with the increase of the original data volume,which is suitable for the prediction of long series time series.

Key words: Informer, Loss function, Kernal trick, Long series time series prediction