Economic Development Prediction Model Based on Deep Convolutional Neural Network

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.