Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets

Washington R. Padilla, Garcia H. Jesus, Jose M. Molina

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

This research explores the ability to extract knowledge about the associations among agricultural products which allows to improve the prediction of future consumption in the local markets of the Andean region of Ecuador. This commercial activity is carried out using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups. The fusion of information from spatially located heterogeneous data sources allows to establish the best association rules between data sources (several products in several local markets) to infer a significant improvement in spatial prediction accuracy for sales future agricultural products.

Original languageEnglish
Title of host publication2018 21st International Conference on Information Fusion, FUSION 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2407-2414
Number of pages8
ISBN (Print)9780996452762
DOIs
StatePublished - 5 Sep 2018
Event21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom
Duration: 10 Jul 201813 Jul 2018

Publication series

Name2018 21st International Conference on Information Fusion, FUSION 2018

Conference

Conference21st International Conference on Information Fusion, FUSION 2018
Country/TerritoryUnited Kingdom
CityCambridge
Period10/07/1813/07/18

Keywords

  • alternative circuits of commercialization
  • association rules
  • cokriging
  • data mining
  • kriging
  • predictive analysis
  • spatial prediction
  • time series

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