Artificial Hydrocarbon Networks for Online Sales Prediction

• Published in: Advances in Artificial Intelligence and Its Applications pp. 498 - 508
• Main Authors: Ponce, Hiram, Miralles-Pechúan, Luis, de Lourdes Martínez-Villaseñor, María
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Artificial hydrocarbon networks; Artificial organic networks; Prediction of online sales; Sales forecasting; Supervised regression
• ISBN: 3319271008; 9783319271002
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-27101-9_38

Online retail sales have been growing worldwide in the last decade. In order to cope with this high dynamicity and market share competition, online retail sales prediction and online advertising have become very important to answer questions of pricing decisions, advertising responsiveness, and product demand. To make adequate investment in products and channels it is necessary to have a model that relates certain features of the product with the number of sales that will occur in the future. In this paper we describe a comparative analysis of machine learning techniques against a novel supervised learning technique called artificial hydrocarbon networks (AHN). This method is a new type of machine learning that have proved to adapt very well to a wide spectrum of problems of regression and classification. Thus, we use artificial hydrocarbon networks for predicting the number of online sales, and then we compare their performance with other ten well-known methods of machine learning regression, obtaining promising results.