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Research on Commodities Constraint Optimization Based on Graph Neural Network Prediction
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DOI：10.1109/ACCESS.2023.3302923.png)
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摘要
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Business intelligence makes good sale prediction crucial in any commercial activity as it has a significant impact on production and supply plan. However, practical commercial data presents explicit constraints, that how to get the optimal forecasts of commodity sales under the constraints is a vital problem many researchers face. The present research proposes a prediction model which combines graph convolution neural network and node bipartite graph. Firstly, the node bipartite graph algorithm is used to merge the constraint graph and the store graph, obtaining the store-constraint bipartite graph. Secondly, a graph convolutional neural network integrating GRU and AR is utilized to extract temporal features (X). Finally, a fully connected network is applied to predict the optimal solution (Y) after constraint optimization. The former can effectively learn complex features of stores, meanwhile, the later combines the constraint conditions with the store, which can effectively predict the sales of goods under the constraint conditions. In terms of model performance, we compared the proposed model with the classical method such as SVR, LSTM, ARIMA. RMSE, MSE, MAE and MAPE are used for evaluation indexes, and the results show that MAPE for one month's sales of some product from both datasets is 7.75%.
Keyword:
Graph convolutional networks
constraint optimization
graph representation learning
sales prediction
business intelligence
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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Root mean square error (RMSE) or mean absolute error (MAE)? - Arguments against avoiding RMSE in the literature均方根误差 (RMSE) 或平均绝对误差 (MAE)？-反对在文献中避免RMSE的论点
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