Sportswear retailing forecast model based on the combination of multi-layer perceptron and convolutional neural network

Abstract

Apparel sales forecasting plays an important role in production planning, distribution decision, and inventory management of enterprises. Especially, the sportswear market has been shown rapid growth characterized by long-term sales. This paper proposes a sales forecasting model for sportswear sales based on the multi-layer perceptron (MLP) and the convolutional neural network (CNN). A novel loss function is also proposed to improve the prediction accuracy. The proposed model is trained and validated on the time-series retailing data collected from three offline local sports stores in China. The influencing factors of retailing forecasting, such as time-series sales data, product features, distribution strategy, shop size, and other parameters, were also defined. Experimental results show that the proposed forecasting model outperforms the compared statistical methods by a large margin. Specifically, the proposed model provided 65% prediction accuracy, while the compared methods provided 16% prediction accuracy. The results show that the proposed model could be potentially used in sportswear sales forecasting, especially offline clothing and other long lifecycle clothing fields.

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