Time Series Prediction Method Based on Improved Attention Mechanism and GRU
HU Xiang
XU Xiaozhong
Abstract：Gated recurrent unit(GRU)network has a weak ability to capture information in time series dimension.To solve this problem,a time series prediction model based on the integration of improved attention mechanism and GRU network is pro-posed.The model adopts the sequence to sequence(Seq2Seq)structure,and the improved attention mechanism uses distance corre-lation coefficient as the evaluation function to assign weight to the output of each historical moment of the coding layer,so as to adap-tively enhance the influence of key historical moments,thus strengthening the information capture ability of GRU network in the time series dimension.By using the gas load data of a certain area in Shanghai for prediction analysis,the experimental results show that the prediction effect of this model is better than other common models,which proves the feasibility of this model and provides a new idea for time series prediction based on GRU network.
Keywords：GRUtime series predictiondistance correlation coefficientattention mechanismevaluation function
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：5( 3407-3411 )