Research Article
A Deep Learning-Based Method for Forecasting Retail Prices of Internationally Traded Goods
@INPROCEEDINGS{10.1007/978-3-032-00303-4_6, author={Yi Qu and Yi Liu}, title={A Deep Learning-Based Method for Forecasting Retail Prices of Internationally Traded Goods}, proceedings={Advanced Hybrid Information Processing. 8th International Conference, ADHIP 2024, Jiaxing, China, September 20--22, 2024, Proceedings, Part III}, proceedings_a={ADHIP}, year={2026}, month={11}, keywords={Deep learning International trade Retail price Price forecast Convolutional neural network Door control cycle unit CNN-GRU model}, doi={10.1007/978-3-032-00303-4_6} }- Yi Qu
Yi Liu
Year: 2026
A Deep Learning-Based Method for Forecasting Retail Prices of Internationally Traded Goods
ADHIP
Springer
DOI: 10.1007/978-3-032-00303-4_6
Abstract
The development of international trade makes the fluctuation of commodity retail prices increasingly complex. Traditional commodity price forecasting methods are often based on statistical models and linear regression methods, which are difficult to deal with large-scale, high-dimensional data, and difficult to capture the nonlinear characteristics of price fluctuations. Therefore, an in-depth learning based forecasting method for commodity retail prices in international trade is proposed. The preparation of sample data is completed by acquiring and preprocessing the data of influencing factors of retail prices of international trade commodities, and a CNN-GRU model is constructed by combining the convolutional neural network and gating cycle unit in the in-depth learning, in which the sample data is input to realize the prediction of future retail prices of international trade commodities. The experimental results show that when the design method is used to predict the retail price of international trade commodities, it can accurately and stably predict the retail price of commodities, and achieve the expected effect.