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

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

doi:10.23919/ICIF.2018.8455594

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

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

Spatial Data Infrastructure Research Group Artificial Intelligence Geoportals and Applied Computing (IDE IA GEO CA)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	2018 21st International Conference on Information Fusion, FUSION 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2407-2414
Number of pages	8
ISBN (Print)	9780996452762
DOIs	https://doi.org/10.23919/ICIF.2018.8455594
State	Published - 5 Sep 2018
Event	21st International Conference on Information Fusion, FUSION 2018 - Cambridge, United Kingdom Duration: 10 Jul 2018 → 13 Jul 2018

Publication series

Name	2018 21st International Conference on Information Fusion, FUSION 2018

Conference

Conference	21st International Conference on Information Fusion, FUSION 2018
Country/Territory	United Kingdom
City	Cambridge
Period	10/07/18 → 13/07/18

Bibliographical note

Publisher Copyright:
© 2018 ISIF

Keywords

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

Access to Document

10.23919/ICIF.2018.8455594

Cite this

Padilla, W. R., Jesus, G. H., & Molina, J. M. (2018). Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets. In 2018 21st International Conference on Information Fusion, FUSION 2018 (pp. 2407-2414). Article 8455594 (2018 21st International Conference on Information Fusion, FUSION 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ICIF.2018.8455594

Padilla, Washington R. ; Jesus, Garcia H. ; Molina, Jose M. / Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets. 2018 21st International Conference on Information Fusion, FUSION 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 2407-2414 (2018 21st International Conference on Information Fusion, FUSION 2018).

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title = "Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets",

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.",

keywords = "alternative circuits of commercialization, association rules, cokriging, data mining, kriging, predictive analysis, spatial prediction, time series",

author = "Padilla, {Washington R.} and Jesus, {Garcia H.} and Molina, {Jose M.}",

note = "Publisher Copyright: {\textcopyright} 2018 ISIF; 21st International Conference on Information Fusion, FUSION 2018 ; Conference date: 10-07-2018 Through 13-07-2018",

year = "2018",

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day = "5",

doi = "10.23919/ICIF.2018.8455594",

language = "English",

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series = "2018 21st International Conference on Information Fusion, FUSION 2018",

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Padilla, WR, Jesus, GH & Molina, JM 2018, Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets. in 2018 21st International Conference on Information Fusion, FUSION 2018., 8455594, 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2407-2414, 21st International Conference on Information Fusion, FUSION 2018, Cambridge, United Kingdom, 10/07/18. https://doi.org/10.23919/ICIF.2018.8455594

Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets. / Padilla, Washington R.; Jesus, Garcia H.; Molina, Jose M.
2018 21st International Conference on Information Fusion, FUSION 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 2407-2414 8455594 (2018 21st International Conference on Information Fusion, FUSION 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets

AU - Padilla, Washington R.

AU - Jesus, Garcia H.

AU - Molina, Jose M.

PY - 2018/9/5

Y1 - 2018/9/5

N2 - 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.

AB - 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.

KW - alternative circuits of commercialization

KW - association rules

KW - cokriging

KW - data mining

KW - kriging

KW - predictive analysis

KW - spatial prediction

KW - time series

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U2 - 10.23919/ICIF.2018.8455594

DO - 10.23919/ICIF.2018.8455594

M3 - Conference contribution

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SN - 9780996452762

T3 - 2018 21st International Conference on Information Fusion, FUSION 2018

SP - 2407

EP - 2414

BT - 2018 21st International Conference on Information Fusion, FUSION 2018

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 21st International Conference on Information Fusion, FUSION 2018

Y2 - 10 July 2018 through 13 July 2018

ER -

Padilla WR, Jesus GH, Molina JM. Model Learning and Spatial Data Fusion for Predicting Sales in Local Agricultural Markets. In 2018 21st International Conference on Information Fusion, FUSION 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 2407-2414. 8455594. (2018 21st International Conference on Information Fusion, FUSION 2018). doi: 10.23919/ICIF.2018.8455594

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

Abstract

Publication series

Conference

Bibliographical note

Keywords

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