Analysis on the impacts of changes in China's peripheral diplomatic relations on export trade using integrated ARIMA-LSTM model

Import and export commerce is a significant strategy for stimulating national economic growth and realizing foreign exchange. Based on the above, this research work dives into the delicate link between changes in China's peripheral diplomatic relations and their resultant consequences on export commerce in China's quickly developing global environment. It provides a unique combined forecasting model that combines autoregressive integrated moving average (ARIMA) time-series analysis with long short-term memory (LSTM) networks to answer the pressing demand for accurate forecasts in this dynamic setting. First, a forecasting model is established for the influencing factors and volume of import and export trade of ASEAN. Second, it establishes the regression equation with the econometrics method by analyzing the specific impact of each influencing factor on the forecasting target. Moreover, it uses data statistics to mathematically process the historical data of ASEAN's import and export volume by determining the correlation between ASEAN's trade volume and some influencing factors. Regression analysis is often used to find relationships between variables. Adding ARIMA time-series modeling improves the model's prediction capabilities, providing a comprehensive insight into China-ASEAN import and export trade volume dynamics. Finally, it compares the performance of the suggested forecasting model to existing approaches, which proves that the correlation rate is enhanced to above 90%. In contrast, other error rates, including the average root, are decreased to the square error. These results show that the prediction model based on the combination of time series and LSTM produces the best short-term forecast of China and ASEAN import and export volume.