A Trend Forecast of Import and Export Trade Total Volume based on LSTM. (October 2019)

• Record Type: Journal Article
• Authors: Qu, Qinglin; Li, Zhao; Tang, Juanjuan; Wu, Shiwei; Wang, Ruishuang
• Is Part Of: IOP conference series. Volume 646(2019)
• Publication Date: 2019-10
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/646/1/012002
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The monthly import and export data are usually with the challenging characteristics including large scale, nonlinear and hard to fit, leading to their development trends can be hardly predicted. To tackle this problem, we propose to leverage the LSTM-based recurrent neural network to forecast the development trend in this paper. We demonstrate the effectiveness of our approach based on the monthly import and export data of Shandong Province from January 2001 to June 2018. In particular, we achieve the MSE score of 124.39, which outperforms the traditional time series model less than 12.2%.