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

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

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.

Idioma originalInglés
Título de la publicación alojada2018 21st International Conference on Information Fusion, FUSION 2018
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas2407-2414
Número de páginas8
ISBN (versión impresa)9780996452762
DOI
EstadoPublicada - 5 sep. 2018
Evento21st International Conference on Information Fusion, FUSION 2018 - Cambridge, Reino Unido
Duración: 10 jul. 201813 jul. 2018

Serie de la publicación

Nombre2018 21st International Conference on Information Fusion, FUSION 2018

Conferencia

Conferencia21st International Conference on Information Fusion, FUSION 2018
País/TerritorioReino Unido
CiudadCambridge
Período10/07/1813/07/18

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© 2018 ISIF

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