Construction of fast fashion clothing sales prediction model based on deep learning
In order to accurately predict the sales volume of fast fashion clothing products and capture the time information in the intermittent or abnormal peak sales volume,based on the deep autoregressive model,the time attention mechanism was introduced,the network structure design was improved,and the global timing model was constructed to predict the sales volume of fast fashion clothing products.The study found that the deep autoregressive model based on the attention mechanism can effectively learn the time correlation between the normal sales value of clothing products and the intermittent or abnormal peak value from all sales data,and can identify the long-term trend and short-term fluctuations of product sales under complex patterns,and its performance is better than other classical models.The feasibility of constructing fast fashion clothing sales forecasting model based on deep learning was verified.
deep learningsales predictiondata drivenfast fashionAT-DeepAR model
NETL
NSTL
万方数据
维普
