Summary of Cross-border Commodity Pricing Strategy Optimization Via Mixed Neural Network For Time Series Analysis, by Lijuan Wang and Yijia Hu and Yan Zhou
Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis
by Lijuan Wang, Yijia Hu, Yan Zhou
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Engineering, Finance, and Science (cs.CE); General Economics (econ.GN)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed CNN-BiGRU-SSA model is a hybrid neural network that effectively predicts and optimizes cross-border commodity pricing strategies by analyzing time series data. This method outperforms existing approaches in accurately forecasting market dynamics and trends, as demonstrated through experimental validation on multiple datasets, including UNCTAD, IMF, WITS, and China Customs. The model’s performance advantages include reduced MAE to 4.357, RMSE to 5.406, and R2 to 0.961 on the UNCTAD dataset, as well as similar excellent results on the IMF and WITS datasets. This study provides a valuable reference for enterprises seeking to formulate more effective cross-border commodity pricing strategies, thereby enhancing market competitiveness and profitability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new method called CNN-BiGRU-SSA is used to predict prices of goods traded across borders. This helps businesses make good decisions about what to buy or sell. The model uses special kinds of data called time series data, which shows patterns and trends in how things happen over time. When tested on different datasets, the method did very well, making it useful for real-world applications. |
Keywords
» Artificial intelligence » Cnn » Mae » Neural network » Time series
