Artificial Hydrocarbon Networks for Online Sales Prediction

Ponce, Hiram; Miralles-Pechuán, Luis; Martinez-Villaseñor, Lourdes

doi:10.1007/978-3-319-27101-9_38

Artificial Hydrocarbon Networks for Online Sales Prediction

Journal

Advances in Artificial Intelligence and Its Applications

Lecture Notes in Computer Science

ISSN

0302-9743

1611-3349

Date Issued

2015

Author(s)

Miralles-Pechuán, Luis

Type

Resource Types::text::book::book part

DOI

10.1007/978-3-319-27101-9_38

URL

https://scripta.up.edu.mx/handle/20.500.12552/4470

Abstract

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.