基于关联规则及组合模型的面料需求预测.

Due to the complexity of the fabric composition of clothing, the demand of enterprises for fabrics of different specifications and models is inconsistent at different times, and the traditional manual prediction and one-dimensional intelligent prediction models are difficult to solve the problem. Aiming at the pain points of uncertain and difficult prediction of fabric demand in garment enterprises, a fabric demand prediction method based on association rules and combination model was proposed. Firstly, Apriori fabric type association model was constructed to mine the type association rules between multiple batches and categories of fabrics. Then, a combined prediction model of Prophet time series model and long short term memory neural network（LSTM） was constructed, which combined its advantages in solving the problem of fabric demand prediction. Finally, taking the historical demand data of highly correlated fabric models as input, the weight coefficients of the combined model were optimized by quantum particle swarm optimization（QPSO） to predict the demand of correlated fabrics. Using root mean squared error（RMSE） and mean absolute error（MAE） as evaluation indexes to design comparative experiments, the experimental results show that the QPSOProphet LSTM fabric demand prediction model using quantum particle swarm optimization RMSE is 5.464 lower than prophet and 1.184 lower than LSTM. MAE is 4.261 lower than prophet and 0.819 lower than LSTM, and the demand prediction accuracy is higher, which supports the flexible production of fabric in garment enterprises. [ABSTRACT FROM AUTHOR]