A Time Series-based Statistical Approach for Trade Turnover Forecasting and Assessing: Evidence from China and Russia

• 발행기관: 한국유통과학회
• 저자명: Xiao Wei DING
• 간행물 정보: 『The Journal of Asian Finance, Economics and Business(JAFEB)』The Journal of Asian Finance, Economics, and Business Vol. 9 No.4, 83~92쪽, 전체 10쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2022.04.30

영문 초록

Due to the uncertainty in the order of the integrated model, the SARIMA-LSTM model, SARIMA-SVR model, LSTM-SARIMA model, and SVR-SARIMA model are constructed respectively to determine the best-combined model for forecasting the China-Russia trade turnover. Meanwhile, the effect of the order of the combined models on the prediction results is analyzed. Using indicators such as MAPE and RMSE, we compare and evaluate the predictive effects of different models. The results show that the SARIMA-LSTM model combines the SARIMA model’s short-term forecasting advantage with the LSTM model’s long-term forecasting advantage, which has the highest forecast accuracy of all models and can accurately predict the trend of China-Russia trade turnover in the post-epidemic period. Furthermore, the SARIMA - LSTM model has a higher forecast accuracy than the LSTM-ARIMA model. Nevertheless, the SARIMA-SVR model’s forecast accuracy is lower than the SVR-SARIMA model’s. As a result, the combined models’ order has no bearing on the predicting outcomes for the China-Russia trade turnover time series.

목차

1. Introduction

2. Literature Review

3. Data and Model Specification

4. Results and Discussion

5. Conclusion and Implications

References

키워드

Time Series China-Russia Trade Turnover Forecast Accuracy Combined Model