A method of forecasting trade export volume based on back-propagation neural network

Abstract

Financial forecasting has been greatly improved in recent years, but at long horizons, forecast accuracy may be low. Foreign trade plays an important role in introducing advanced technology and equipment, expanding employment opportunities, increasing government revenue and promoting economic growth. The main purpose of this paper is to predict the export volume of foreign trade through a back-propagation neural network (BPNN). To shed light on the characteristics of foreign trade and the export volume calculation method, this paper uses BPNN for forecasting. This method has a unique and advanced advantage in solving nonlinear problems and is very suitable for solving forecasting and decision-making problems related to nonlinear financial systems. By establishing multifactor and single-factor export forecasting models, the export volume of a single Chinese city in recent years is forecasted and compared with the actual export volume. The forecasting accuracy of our model is more than 30% higher than that of the traditional forecasting method, and the application is also approximately 15% more accurate than the traditional method, indicating that the method used in this paper is more in line with the growth trend of the actual export data. As a key part of the economic system, foreign trade is an important force driving economic growth. Therefore, developing foreign trade is a suitable path to pursue growth.

Forecasting the Impact of the COVID-19 Outbreak on China’s Cotton Exports by Modified Discrete Grey Model with Limited Data

“…government intervention, economic, social and environmental policies) appeared to affect the prediction of cotton exports, resulting in a typical grey system. 24 , 39 In reviewing previous research, several forecasting tools have been proposed to solve the problem of nonlinear export trade predictions, including the time series model, 9 , 10 regression model, 11 , 12 artificial neural networks, 13 – 15 SVM, 16 ARIMA, 17 and hybrid forecasting models. 18 – 20 Fluctuations in the annual data are smaller than those in the monthly data, making it difficult to identify effective strategies for dealing with nonlinear monthly data with long delays.…”

“…These pandemic outbreaks have severely threatened the supply chain management of the textile industry by creating uncertainty and challenges in the export trade system. Several forecasting tools, including the time series model, 9 , 10 regression model, 11 , 12 artificial neural networks, 13 – 15 support vector machines (SVM), 16 autoregressive integrated moving average model (ARIMA) 17 and hybrid forecasting models, 18 – 20 have been put forward to solve the nonlinear export trade prediction problem. Although the above techniques can achieve high levels of prediction accuracy based on abundant data, these unavoidable limitations cannot be overlooked in practice.…”