[Paper Review] Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis

Lijuan Wang, Yucai Hu+1 more|arXiv (Cornell University)|Aug 22, 2024

Stock Market Forecasting MethodsDecision Sciences3 citations

TL;DR

This paper proposes a hybrid CNN-BiGRU-SSA neural network model to optimize cross-border commodity pricing strategies by leveraging time series analysis. The model achieves state-of-the-art performance, reducing MAE to 4.357 and RMSE to 5.406 on the UNCTAD dataset, with an R² of 0.961, significantly outperforming existing methods 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.

Motivation & Objective

• To address the limitations of existing pricing strategies in dynamic international markets by improving prediction accuracy and agility.
• To develop a deep learning model capable of capturing complex temporal patterns in cross-border commodity price time series.
• To optimize pricing strategies based on accurate forecasting, enhancing market competitiveness and profitability.
• To validate the model’s effectiveness across diverse, real-world international trade datasets.
• To establish a foundation for deep learning applications in international trade and economic strategy optimization.

Proposed method

• The proposed model integrates Convolutional Neural Networks (CNN) for local feature extraction from time series data.
• Bidirectional Gated Recurrent Units (BiGRU) are used to capture long-range dependencies and temporal dynamics in both forward and backward directions.
• Salp Swarm Algorithm (SSA) optimizes the hyperparameters of the deep learning model to improve convergence and generalization.
• The hybrid architecture combines CNN’s spatial feature learning with BiGRU’s sequential modeling, enhanced by SSA for optimal parameter tuning.
• The model is trained end-to-end on multivariate time series data from international trade sources such as UNCTAD, IMF, WITS, and China Customs.
• Performance is evaluated using standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R².

Experimental results

Research questions

• RQ1 Can a hybrid deep learning model improve the accuracy of cross-border commodity price forecasting compared to traditional methods?
• RQ2 How effective is the integration of CNN, BiGRU, and SSA in capturing complex temporal patterns in international trade time series?
• RQ3 To what extent does the proposed model outperform existing models on diverse, real-world trade datasets?
• RQ4 Can the optimized pricing strategy derived from the model enhance market competitiveness and profitability in cross-border trade?
• RQ5 What is the generalization capability of the model across different international trade data sources?

Key findings

• On the UNCTAD dataset, the model achieves a MAE of 4.357, RMSE of 5.406, and R² of 0.961, demonstrating superior predictive accuracy.
• The model significantly outperforms baseline models on the IMF and WITS datasets, with consistent improvements in MAE, RMSE, and R².
• The integration of SSA for hyperparameter optimization enhances model convergence and reduces overfitting across all datasets.
• The CNN-BiGRU-SSA model exhibits strong generalization across diverse data sources, including UNCTAD, IMF, WITS, and China Customs.
• The results confirm the model’s reliability and effectiveness for real-world cross-border commodity pricing strategy optimization.
• The study establishes a robust deep learning framework with practical value for international trade and economic strategy applications.