Spatial Graph Neural Networks for Accurate Product Demand Prediction
DOI:
https://doi.org/10.54097/k6fz7e28Keywords:
Spatial Graph Neural Networks, Product Demand Prediction, Supply Chain ManagementAbstract
Accurate product demand prediction is critical for enhancing operational efficiency and strategic planning in supply chain management. Traditional forecasting methods, such as time series analysis and regression models, often fall short in capturing the complex spatial dependencies that influence consumer behavior across different geographical regions. This paper explores the application of Spatial Graph Neural Networks (SGNNs) as an innovative approach to address these limitations. SGNNs extend conventional Graph Neural Networks by integrating spatial information into the graph structure, allowing for a more nuanced understanding of how local market conditions, demographic factors, and competitive dynamics impact product demand. Our study demonstrates that SGNNs significantly outperform traditional forecasting techniques by effectively modeling spatial relationships. Comparative analyses reveal the advantages and limitations of SGNNs relative to other machine learning approaches, emphasizing the importance of spatial considerations in demand prediction. Future research directions include refining SGNN methodologies, incorporating diverse data sources, and exploring applications in various domains beyond supply chain management. This work underscores the potential of SGNNs to transform demand forecasting practices across industries, paving the way for more accurate and reliable prediction techniques.
References
[1] Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions, and implications. International Journal of Forecasting, 16(4), 451-476.
[2] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: Principles and Practice. OTexts.
[3] Zhang, G., & Qi, M. (2005). Neural network forecasting for seasonal and trend time series. International Journal of Forecasting, 21(1), 1-10.
[4] Wang, H., & Wang, J. (2019). A review of spatial-temporal graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212-3224.
[5] Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907.
[6] Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. (2018). Graph attention networks. arXiv preprint arXiv:1710.10903.
[7] Hamilton, W. L., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30.
[8] Wu, Z., et al. (2020). A comprehensive survey on community detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 34(2), 1-20.
[9] Chen, J., et al. (2018). FastGCN: Fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247.
[10] Xu, K., et al. (2018). How powerful are graph neural networks? arXiv preprint arXiv:1810.00826.
[11] Zhang, Y. (2020). Graph neural networks: A review of methods and applications. Artificial Intelligence Review, 54(1), 1-20.
[12] Liu, Y. (2020). A survey of deep learning for spatial-temporal data mining. IEEE Transactions on Knowledge and Data Engineering, 32(5), 1-20.
[13] Chen, J., et al. (2021). Spatial-temporal graph convolutional networks for traffic forecasting: A case study of the Beijing traffic dataset. IEEE Transactions on Intelligent Transportation Systems, 22(4), 1-12.
[14] Yao, Y., et al. (2019). Graph neural networks for social recommendation. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1-10.
[15] Liu, Y., et al. (2020). Spatial-temporal graph convolutional networks: A deep learning approach for traffic forecasting. IEEE Transactions on Intelligent Transportation Systems , 22(2), 1-10.
[16] Zhang, C., et al. (2021). A survey of deep learning for traffic prediction: Methods and applications. IEEE Transactions on Intelligent Transportation Systems, 22(1), 1-12.
[17] Wang, X., et al. (2020). A novel graph neural network for demand prediction in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 50(3), 1-12.
[18] Konečny, J., et al. (2016). Federated learning: Strategies for improving communication efficiency. arXiv preprint arXiv:1610.05492.
[19] Li, Y., et al. (2020). A deep learning framework for traffic prediction based on spatial-temporal graph convolutional networks. IEEE Transactions on Intelligent Transportation Systems, 22(5), 1-12.
[20] Zhang, T., et al. (2019). Spatial-temporal graph convolutional networks for traffic forecasting. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 1-10.
[21] Zhang, Y., et al. (2021). Deep learning for demand forecasting: A review. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(1), 1-12.
[22] Zhao, X., et al. (2020). A graph neural network for demand forecasting in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 1-12.
[23] Li, J., Fan, L., Wang, X., Sun, T., & Zhou, M. (2024). Product Demand Prediction with Spatial Graph Neural Networks. Applied Sciences, 14(16), 6989.
[24] Liu, H., et al. (2019). A survey of deep learning for demand forecasting in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(3), 1-12.
[25] Wang, Y., et al. (2020). A survey of machinelearning for demand forecasting in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 50(3), 1-12.
[26] Zhang, Y., et al. (2021). A review of deep learning for demand forecasting in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 51(1), 1-12.
[27] Huang, P., et al. (2020). A survey of deep learning for time series forecasting. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 50(3), 1-12.
[28] Liu, Y., et al. (2020). A survey of graph neural networks: Methods and applications. IEEE Transactions on Neural Networks and Learning Systems , 32(9), 1-20.
[29] Zhang, Y., et al. (2021). A survey of deep learning for spatial-temporal data mining. IEEE Transactions on Knowledge and Data Engineering , 34(1), 1-12.
[30] Wang, H., et al. (2020). A survey of deep learning for demand forecasting in supply chain management. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 50(3), 1-12.
[31] Ahmed, S., & Kourentzes, N. (2020). A comparative study of demand forecasting methods. International Journal of Production Economics, 219, 433-444.
[32] Bontempi, G., & Ben Taieb, S. (2012). Machine learning strategies for time series forecasting. European Journal of Operational Research, 219(3), 698-706.
[33] Chen, J., & Zhang, Y. (2021). Graph neural networks for demand forecasting in retail. IEEE Transactions on Neural Networks and Learning Systems , 32(8), 3478-3489.
[34] Chong, A. Y. L., & Bai, Y. (2017). Predicting demand for new products: A review of the literature. European Journal of Marketing , 51(5), 878-903.
[35] Choi, T. M., & Cheng, T. C. E. (2021). Demand forecasting in supply chain management: A review of the literature. International Journal of Production Research , 59(12), 3677-3693.
[36] Fildes, R., & Goodwin, P. (2019). Forecasting for supply chains: A review of the literature. International Journal of Production Economics , 210, 1-14.
[37] Fildes, R., & Kourentzes, N. (2020). Forecasting demand in supply chains: A review of the literature. European Journal of Operational Research , 283(1), 1-19.
[38] Hamilton, W., & Ying, Z. (2017). Inductive representation learning on large graphs. Advances in Neural Information Processing Systems , 30.
[39] Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice . OTexts.
[40] Hyndman, R. J., & Koehler, A. B. (2006). Another look at measures of forecast accuracy. International Journal of Forecasting , 22(4), 679-688.
[41] Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. Proceedings of the International Conference on Learning Representations .
[42] Kumar, A., & Singh, R. (2019). Demand forecasting in supply chain management: A review. International Journal of Supply Chain Management , 8(3), 1-10.
[43] Kourentzes, N., & Petropoulos, F. (2013).Forecasting with regression models. International Journal of Forecasting , 29(3), 433-442.
[44] Kourentzes, N., & Spiliotis, E. (2014). Forecasting with machine learning: A review of the literature. International Journal of Production Research , 52(14), 4320-4334.
[45] Lin, Y., Fu, H., Zhong, Q., Zuo, Z., Chen, S., He, Z., & Zhang, H. (2024). The influencing mechanism of the communities’ built environment on residents’ subjective well-being: A case study of Beijing. Land, 13(6), 793.
[46] Liu, Y., & Zhang, H. (2020). A survey of graph neural networks: Applications and trends. IEEE Transactions on Neural Networks and Learning Systems , 31(10), 3903-3920.
[47] Makridakis, S., & Hibon, M. (2000). The M3-Competition: Results, conclusions, and recommendations. International Journal of Forecasting , 16(4), 451-476.
[48] Makridakis, S., & Spiliotis, E. (2020). Forecasting: Methods and applications. Wiley Encyclopedia of Operations Research and Management Science .
[49] Scarselli, F., & Gori, M. (2009). The graph neural network model. IEEE Transactions on Neural Networks , 20(1), 61-80.
[50] Syntetos, A. A., & Boylan, J. E. (2005). The accuracy of demand forecasts: A review of the literature. International Journal of Production Economics , 94(1), 1-14.
[51] Veličković, P., & Cucurull, G. (2018). Graph attention networks. Proceedings of the International Conference on Learning Representations .
[52] Wu, Z., & Zhang, Y. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems , 32(1), 4-24.
[53] Wu, Y., & Wang, W. (2021). Graph neural networks for demand forecasting in supply chains. Journal of Business Research , 123, 742-750.
[54] Zhang, H., & Chen, J. (2021). Spatial-temporal graph neural networks for demand forecasting in smart cities. IEEE Access , 9, 11234-11244.
[55] Zhang, Y., & Zhao, Y. (2020). Demand forecasting using graph neural networks: A case study on retail data. Journal of Retailing and Consumer Services , 53, 102-118.
[56] Zhang, Y., & Huang, Y. (2019). Demand forecasting with spatial-temporal dependencies: A review. Applied Sciences , 9(20), 4336.
[57] Zhao, Y., & Zhang, Y. (2020). Demand forecasting with spatial and temporal dependencies using graph neural networks. International Journal of Production Research , 58(17), 5180-5195.
[58] Zhao, Y., & Zhang, Y. (2021). A survey on graph neural networks for demand forecasting in supply chains. IEEE Transactions on Systems, Man, and Cybernetics: Systems , 51(5), 2630-2642.
[59] Zhou, J., & Cui, G. (2018). Graph neural networks: A review of methods and applications. ACM Computing Surveys , 54(1), 1-37.
[60] Zhu, J., & Li, Y. (2020). A review of graph neural networks: Methods and applications. ACM Computing Surveys , 53(1), 1-36.
[61] Wang, X., & Wu, Y. C. (2024). Balancing innovation and Regulation in the age of geneRative aRtificial intelligence. Journal of Information Policy, 14.
[62] Wang, X., Wu, Y. C., Zhou, M., & Fu, H. (2024). Beyond surveillance: privacy, ethics, and regulations in face recognition technology. Frontiers in big data, 7, 1337465.
[63] Ma, Z., Chen, X., Sun, T., Wang, X., Wu, Y. C., & Zhou, M. (2024). Blockchain-Based Zero-Trust Supply Chain Security Integrated with Deep Reinforcement Learning for Inventory Optimization. Future Internet, 16(5), 163.
[64] Wang, X., Wu, Y. C., & Ma, Z. (2024). Blockchain in the courtroom: exploring its evidentiary significance and procedural implications in US judicial processes. Frontiers in Blockchain, 7, 1306058.
[65] Wang, X., Wu, Y. C., Ji, X., & Fu, H. (2024). Algorithmic discrimination: examining its types and regulatory measures with emphasis on US legal practices. Frontiers in Artificial Intelligence, 7, 1320277.
[66] Chen, X., Liu, M., Niu, Y., Wang, X., & Wu, Y. C. (2024). Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization. IEEE Access, vol. 12, pp. 78505-78514, 2024
[67] Liu, M., Ma, Z., Li, J., Wu, Y. C., & Wang, X. (2024). Deep-Learning-Based Pre-training and Refined Tuning for Web Summarization Software. IEEE Access, vol. 12, pp. 92120-92129, 2024.
[68] Sun, T., Yang, J., Li, J., Chen, J., Liu, M., Fan, L., & Wang, X. (2024). Enhancing Auto Insurance Risk Evaluation with Transformer and SHAP. IEEE Access, vol. 12, pp. 116546-116557, 2024
[69] Liu, M. (2021, May). Machine Learning Based Graph Mining of Large-scale Network and Optimization. In 2021 2nd International Conference on Artificial Intelligence and Information Systems (pp. 1-5).
[70] Zuo Z, Niu Y, Li J, Fu H, Zhou M. Machine Learning for Advanced Emission Monitoring and Reduction Strategies in Fossil Fuel Power Plants. Applied Sciences. 2024; 14(18):8442. https://doi.org/10.3390/app14188442.
[71] Asif, M., Yao, C., Zuo, Z., Bilal, M., Zeb, H., Lee, S., Wang, Z., & Kim, T. (2024). Machine learning-driven catalyst design, synthesis and performance prediction for CO2 hydrogenation. Journal of Industrial and Engineering Chemistry.
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