| Autor: |
Cunbing Li |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Intelligent Systems with Applications, Vol 22, Iss , Pp 200364- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2667-3053 |
| DOI: |
10.1016/j.iswa.2024.200364 |
| Popis: |
Abstracts: The study proposes a cascaded hybrid neural network commodity demand prediction model based on multimodal data. This model aims to improve the accuracy of commodity demand forecasts on e-commerce platforms. By constructing multimodal data feature clusters and utilizing a spatial feature fusion strategy, historical order information, and product evaluation sentiment data are integrated. The model combines the advantages of bi-directional long and short-term memory networks and bi-directional gated recurrent unit networks. The proposed cascaded hybrid strategy-based model significantly enhances accuracy in commodity demand forecasting. Results indicated an average absolute error of 0.1682 and root mean square error of 0.4537 for weekly commodity forecasts. For long-term commodity demand, the average absolute error was 0.8611 with a root mean square error of 8.1938. These outcomes highlight the algorithm's high prediction accuracy, making it valuable for commodity demand prediction on e-commerce platforms and providing a framework for effective inventory management. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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