|Table of Contents|

Regional Economic Forecasting Based on Improved Transformer Sequence Algorithm(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2024年04期
Page:
118-125
Research Field:
计算机科学与技术
Publishing date:

Info

Title:
Regional Economic Forecasting Based on Improved Transformer Sequence Algorithm
Author(s):
Liu Haihong1; Liu Min2; Zhu Anqing3
(1.School of Economics and Management,Guangzhou Nanyang Polytechnic College,Guangzhou 510900,China)
(2.School of Computer and Software,Southwest Petroleum University,Nanchong 637001,China)
(3.School of Management,Jinan University,Guangzhou 510000,China)
Keywords:
regional economic forecasting; Transformer; Copula function; principal component analysis; multi-head attention
PACS:
TP391
DOI:
10.3969/j.issn.1001-4616.2024.04.013
Abstract:
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.

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Last Update: 2024-12-15