Regional Economic Forecasting Based on Improved Transformer Sequence Algorithm
The existing regional economic forecasting model indicators are redundant and ignore the impact of static variables such as industry and region on the forecast results,leading to low forecasting efficiency.In response to the above issues,a regional economic forecasting model based on an improved Transformer time series algorithm is suggested.Firstly,the traditional Transformer model is optimized using a Copula function(Coformer);secondly,the impact indicators of the regional economy are selected,and principal component analysis is performed on them to remove redundant information;then,the reduced-dimensional indicator variables and static variables are used as the input of the Coformer,and the variables are encoded.Finally,the decoder decodes the encoded variables and uses Softmax to output the prediction results of the regional gross domestic product series over the years.The experimental outcome indicates that the designed model has an accuracy of 0.908,which is 15.9%,12.3%and 6.7%higher than the other three models,respectively,demonstrating excellent predictive performance.
万方数据
维普
