Research Article
Economic Development Prediction Model Based on Deep Convolutional Neural Network
@INPROCEEDINGS{10.4108/eai.6-1-2023.2330335, author={Taisong Liu and Xiaona Cai and Songyu Xie and Shanting Tan and Zihan Ying}, title={Economic Development Prediction Model Based on Deep Convolutional Neural Network}, proceedings={Proceedings of the 2nd International Conference on Big Data Economy and Digital Management, BDEDM 2023, January 6-8, 2023, Changsha, China}, publisher={EAI}, proceedings_a={BDEDM}, year={2023}, month={6}, keywords={economic prediction deep convolutional neural network normalization multilayer perceptron computation cost}, doi={10.4108/eai.6-1-2023.2330335} }- Taisong Liu
Xiaona Cai
Songyu Xie
Shanting Tan
Zihan Ying
Year: 2023
Economic Development Prediction Model Based on Deep Convolutional Neural Network
BDEDM
EAI
DOI: 10.4108/eai.6-1-2023.2330335
Abstract
Economic prediction is an essential method for arranging the development strategies and provide reliable data for the managers. However, previous researches about prediction were primary concentrated on the mathematical model by utilizing various statistic or economic theories, which are not considering the real situation parameters including cultural affects, political aspects and real-world factors. In this article, we utilize deep convolutional neural network to train a neural model by utilizing the ten years from 2010 to 2020 economic development parameters and predict the 2021-2022 economic development results in GuangZhou city of China. In our proposed model, the model is consisted by three primary components including pro-processing selector by utilizing the normalization, multiple deep convolutional network and multilayer perceptron to provide the final prediction results. From our extensive experimental steps, we can observe that our proposed mechanism can precisely provide the development tendency in 2021 with acceptable computation cost.