Aiming at the problems of error accumulation， modeling difficulty， and low computational efficiency in long time series prediction， a long time series prediction model based on hybrid self-attention and differentiated normalization， namely HSADN （Hybrid Self-Attention and Differentiated Normalization）， was proposed. Firstly， the model used a stacked multi-head self-attention mechanism in the encoder to capture long-distance dependencies in time series， thereby reducing computational complexity， and used a multi-layer sparse self-attention mechanism in the decoder to dynamically adjust the generation strategy. Secondly， in the encoder， Batch Channel Normalization （BCN） was used to extract， fuse， and reconstruct the features， while in the decoder， Layer Normalization （LN） was adopted to alleviate the gradient vanishing and improve the training stability， generating predicted sequence values. Experimental results show that compared with CALF （Cross-modAl Large Language Model Fine-tuning） model， HSADN has the Mean Squared Error （MSE） and Mean Absolute Error （MAE） of univariate prediction reduced by 6.2% and 6.9% on ECL-960， respectively， and by 13.1% and 2.9% on ETTh-720， respectively； the MSE and MAE of multivariate prediction reduced by 3.5% and 2.6% on ETTm-672， respectively， and by 1.8% and 0.9% on Weather-720， respectively； the running time for univariate and multivariate predictions reduced by an average of 4.6% and 28.7%， respectively.
