TY - GEN
T1 - Artificial hydrocarbon networks for online sales prediction
AU - Ponce, Hiram
AU - Miralles-Pechúan, Luis
AU - Martínez-Villaseñor, María De Lourdes
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
KW - Artificial hydrocarbon networks
KW - Artificial organic networks
KW - Prediction of online sales
KW - Sales forecasting
KW - Supervised regression
UR - https://www.scopus.com/pages/publications/84952685424
U2 - 10.1007/978-3-319-27101-9_38
DO - 10.1007/978-3-319-27101-9_38
M3 - Conference contribution
AN - SCOPUS:84952685424
SN - 9783319271002
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 498
EP - 508
BT - Advances in Artificial Intelligence and Its Applications - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015, Proceedings
A2 - Alcántara, Oscar Herrera
A2 - Lagunas, Obdulia Pichardo
A2 - Figueroa, Gustavo Arroyo
PB - Springer Verlag
T2 - 14th Mexican International Conference on Artificial Intelligence, MICAI 2015
Y2 - 25 October 2015 through 31 October 2015
ER -