{"title":"通过时间序列分析混合神经网络优化跨境商品定价策略","authors":"Lijuan Wang, Yijia Hu, Yan Zhou","doi":"arxiv-2408.12115","DOIUrl":null,"url":null,"abstract":"In the context of global trade, cross-border commodity pricing largely\ndetermines the competitiveness and market share of businesses. However,\nexisting methodologies often prove inadequate, as they lack the agility and\nprecision required to effectively respond to the dynamic international markets.\nTime series data is of great significance in commodity pricing and can reveal\nmarket dynamics and trends. Therefore, we propose a new method based on the\nhybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate\nprediction and optimization of cross-border commodity pricing strategies\nthrough in-depth analysis and optimization of time series data. Our model\nundergoes experimental validation across multiple datasets. The results show\nthat our method achieves significant performance advantages on datasets such as\nUNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our\nmodel reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly\nbetter than other models. On the IMF and WITS datasets, our method also\nachieves similar excellent performance. These experimental results verify the\neffectiveness and reliability of our model in the field of cross-border\ncommodity pricing. Overall, this study provides an important reference for\nenterprises to formulate more reasonable and effective cross-border commodity\npricing strategies, thereby enhancing market competitiveness and profitability.\nAt the same time, our method also lays a foundation for the application of deep\nlearning in the fields of international trade and economic strategy\noptimization, which has important theoretical and practical significance.","PeriodicalId":501273,"journal":{"name":"arXiv - ECON - General Economics","volume":"10 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis\",\"authors\":\"Lijuan Wang, Yijia Hu, Yan Zhou\",\"doi\":\"arxiv-2408.12115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the context of global trade, cross-border commodity pricing largely\\ndetermines the competitiveness and market share of businesses. However,\\nexisting methodologies often prove inadequate, as they lack the agility and\\nprecision required to effectively respond to the dynamic international markets.\\nTime series data is of great significance in commodity pricing and can reveal\\nmarket dynamics and trends. Therefore, we propose a new method based on the\\nhybrid neural network model CNN-BiGRU-SSA. The goal is to achieve accurate\\nprediction and optimization of cross-border commodity pricing strategies\\nthrough in-depth analysis and optimization of time series data. Our model\\nundergoes experimental validation across multiple datasets. The results show\\nthat our method achieves significant performance advantages on datasets such as\\nUNCTAD, IMF, WITS and China Customs. For example, on the UNCTAD dataset, our\\nmodel reduces MAE to 4.357, RMSE to 5.406, and R2 to 0.961, significantly\\nbetter than other models. On the IMF and WITS datasets, our method also\\nachieves similar excellent performance. These experimental results verify the\\neffectiveness and reliability of our model in the field of cross-border\\ncommodity pricing. Overall, this study provides an important reference for\\nenterprises to formulate more reasonable and effective cross-border commodity\\npricing strategies, thereby enhancing market competitiveness and profitability.\\nAt the same time, our method also lays a foundation for the application of deep\\nlearning in the fields of international trade and economic strategy\\noptimization, which has important theoretical and practical significance.\",\"PeriodicalId\":501273,\"journal\":{\"name\":\"arXiv - ECON - General Economics\",\"volume\":\"10 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - General Economics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2408.12115\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - ECON - General Economics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2408.12115","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
在全球贸易背景下,跨境商品定价在很大程度上决定了企业的竞争力和市场份额。时间序列数据对商品定价具有重要意义,可以揭示市场动态和趋势。因此,我们提出了一种基于混合神经网络模型 CNN-BiGRU-SSA 的新方法。目标是通过对时间序列数据的深入分析和优化,实现跨境商品定价策略的准确预测和优化。我们的模型在多个数据集上进行了实验验证。结果表明,我们的方法在贸发会议、国际货币基金组织、WITS 和中国海关等数据集上取得了显著的性能优势。例如,在 UNCTAD 数据集上,我们的模型将 MAE 降至 4.357,RMSE 降至 5.406,R2 降至 0.961,明显优于其他模型。在 IMF 和 WITS 数据集上,我们的方法也取得了类似的优异成绩。这些实验结果验证了我们的模型在跨境商品定价领域的有效性和可靠性。同时,我们的方法也为深度学习在国际贸易和经济战略优化领域的应用奠定了基础,具有重要的理论和实践意义。
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.