E-Commerce Sales Revenues Forecasting by Means of Dynamically Designing, Developing and Validating a Directed Acyclic Graph (DAG) Network for Deep Learning

Abstract

As the digitalization process has become more and more important in our daily lives, during recent decades e-commerce has greatly increased in popularity, becoming increasingly used, therefore representing an extremely convenient alternative to traditional stores. In order to develop and maintain profitable businesses, traders need accurate forecasts concerning their future sales, a very difficult task considering that these are influenced by a wide variety of factors. This paper proposes a novel e-commerce sales forecasting method that dynamically builds a Directed Acyclic Graph Neural Network (DAGNN) for Deep Learning architecture. This will allow for long-term, fine-grained forecasts of daily sales revenue, refined up to the level of product categories. The developed forecasting method provides the e-commerce store owner an accurate forecasting tool for predicting the sales of each category of products for up to three months ahead. The method offers a high degree of scalability and generalization capability due to the dynamically incremental way in which the constituent elements of the DAGNN’s architecture are obtained. In addition, the proposed method achieves an efficient use of data by combining the numerous advantages of its constituent layers, registering very good performance metrics and processing times. The proposed method can be generalized and applied to forecast the sales for up to three months ahead in the case of other e-commerce stores, including large e-commerce businesses.

A Brief Survey of Machine Learning and Deep Learning Techniques for E-Commerce Research

“…Another hybrid model, combining BiLSTM and CNN, considered product attributes and sentiment analysis from customer comments to achieve accurate sales predictions [182]. Additionally, a sales forecasting method used a DAGNN dynamically to predict daily sales revenue for different product categories, offering scalability and generalization [183]. Furthermore, a deep neural framework has demonstrated significant performance gains over traditional and other deep learning models in forecasting E-commerce sales, considering promotion campaigns and competing relationships between products [184].…”

“…Preventing over-fitting and achieving robust generalization is another challenge for machine learning and deep learning in e-commerce [109,180,183]. Ensembling techniques, such as bagging and boosting, combine multiple models to improve the overall performance and reduce over-fitting.…”

A Sales Forecasting Model for New-Released and Short-Term Product: A Case Study of Mobile Phones

“…A novel sales forecasting model is proposed, integrating temporal convolutional networks (TCN) for the robust extraction of deep temporal features, demonstrating superior performance compared to conventional neural network models [31]. Directed Acute Graph Neural Network, consisting of a layer of Convolutional Neural Networks and BiLSTM, showed high predictive performance as a revenue prediction method for e-commerce [32]. A study leverages several machine learning (ML) models, including recurrent neural network (RNN) models, such as LSTM and Temporary Fusion Transformer, to present models for accurate sales forecasting for restaurants.…”