Similarity-based sales forecasting using improved ConvLSTM and prophet

Abstract

Sales forecasting is an important part of e-commerce and is critical to smart business decisions. The traditional forecasting methods mainly focus on building a forecasting model, training the model through historical data, and then using it to forecast future sales. Such methods are feasible and effective for the products with rich historical data while they are not performing as well for the newly listed products with little or no historical data. In this paper, with the idea of collaborative filtering, a similarity-based sales forecasting (S-SF) method is proposed. The implementation framework of S-SF includes three modules in order. The similarity module is responsible for generating top-k similar products of a given new product. We calculate the similarity based on two data types: time series data of sales and text data such as product attributes. In the learning module, we propose an attention-based ConvLSTM model which we called AttConvLSTM, and optimize its loss function with the convex function information entropy. Then AttConvLSTM is integrated with Facebook Prophet model to forecast top-k similar products sales based on their historical data. The prediction results of all top-k similar products will be fused in the forecasting module through operations of alignment and scaling to forecast the target products sales. The experimental results show that the proposed S-SF method can simultaneously adapt to the sales forecasting of mature products and new products, which shows excellent diversity, and the forecasting idea based on similar products improves the accuracy of sales forecasting.