Abstract
This research explores information fusion and data mining techniques and proposes a methodology to improve predictions based on strong associations among agricultural products, which allows prediction for future consumption in local markets in the Andean region of Ecuador using spatial prediction techniques. This commercial activity is performed using Alternative Marketing Circuits (CIALCO), seeking to establish a direct relationship between producer and consumer prices, and promote buying and selling among family groups.
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Acknowledgements
This work was supported in part by Project MINECO TEC2017-88048-C2-2-R and by Commercial Coordination Network, Ministry of Agriculture, Livestock, Aquaculture and Fisheries Ecuador.
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Padilla, W.R., García, J., Molina, J.M. (2018). Information Fusion and Machine Learning in Spatial Prediction for Local Agricultural Markets. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_21
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DOI: https://doi.org/10.1007/978-3-319-94779-2_21
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