Paper Info
Title
Artificial Hydrocarbon Networks for Online Sales Prediction.
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.
Year
DOI
Venue
2015
10.1007/978-3-319-27101-9_38
ADVANCES IN ARTIFICIAL INTELLIGENCE AND ITS APPLICATIONS, MICAI 2015, PT II
Keywords
Field
DocType
Prediction of online sales,Artificial organic networks,Artificial hydrocarbon networks,Supervised regression,Sales forecasting
Computer science,Communication channel,Online advertising,Supervised learning,Sales forecasting,Artificial intelligence,Market share,Machine learning
Conference
Volume
ISSN
Citations 
9414
0302-9743
0
PageRank 
References 
Authors
0.34
1
3
Name
Order
Citations
PageRank
Hiram E. Ponce12613.63
Luis Miralles Pechuán231.05
María de Lourdes Martínez-Villaseñor3268.35