Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis

TL;DR

This paper introduces a hybrid neural network model combining CNN, BiGRU, and SSA to accurately predict and optimize cross-border commodity pricing strategies using time series data, validated across multiple international datasets.

Contribution

The study presents a novel hybrid neural network approach for cross-border commodity pricing prediction, demonstrating superior performance over existing models on real-world datasets.

Findings

Significant reduction in MAE and RMSE on UNCTAD dataset

High R2 score indicating strong predictive accuracy

Effective application across multiple international trade datasets

Abstract

In the context of global trade, cross-border commodity pricing largely determines the competitiveness and market share of businesses. However, existing methodologies often prove inadequate, as they lack the agility and precision required to effectively respond to the dynamic international markets. Time series data is of great significance in commodity pricing and can reveal market dynamics and trends. Therefore, we propose a new method based on the hybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate prediction and optimization of cross-border commodity pricing strategies through in-depth analysis and optimization of time series data. Our model undergoes experimental validation across multiple datasets. The results show that our method achieves significant performance advantages on datasets such as UNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our model reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly better than other models. On the IMF and WITS datasets, our method also achieves similar excellent performance. These experimental results verify the effectiveness and reliability of our model in the field of cross-border commodity pricing. Overall, this study provides an important reference for enterprises to formulate more reasonable and effective cross-border commodity pricing strategies, thereby enhancing market competitiveness and profitability. At the same time, our method also lays a foundation for the application of deep learning in the fields of international trade and economic strategy optimization, which has important theoretical and practical significance.