Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis
<|reference_start|>Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis: 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.<|reference_end|>
@article{wang2024cross-border,
    title={Cross-border Commodity Pricing Strategy Optimization via Mixed Neural
  Network for Time Series Analysis},
    author={Lijuan Wang and Yijia Hu and Yan Zhou},
    journal={arXiv preprint arXiv:2408.12115},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12115},
    primaryClass={cs.LG cs.CE econ.GN q-fin.EC}
}