Enhancing sales forecasting by using neuro networks and the popularity of magazine article titles

Hani A. Omar*, Duen-Ren Liu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In this paper, we examine how the popularity information of magazines can be useful for sales forecasting. We propose a sales forecasting model based on Back Propagation Neural Network (BPNN) where the inputs are historical sales and the popularity indexes of magazine article titles. Our proposed model using the popularity of magazine article titles in the forecasting process can improve the accuracy of sales forecasting.

Original languageEnglish
Title of host publicationProceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
Pages577-580
Number of pages4
DOIs
StatePublished - 25 Aug 2012
Event2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 - Kitakyushu, Japan
Duration: 25 Aug 201228 Aug 2012

Publication series

NameProceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012

Conference

Conference2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012
CountryJapan
CityKitakyushu
Period25/08/1228/08/12

Keywords

  • Forecasting
  • Google search engine
  • Neural-network
  • Pupularity

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  • Cite this

    Omar, H. A., & Liu, D-R. (2012). Enhancing sales forecasting by using neuro networks and the popularity of magazine article titles. In Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012 (pp. 577-580). [6457118] (Proceedings - 2012 6th International Conference on Genetic and Evolutionary Computing, ICGEC 2012). https://doi.org/10.1109/ICGEC.2012.87