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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