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Sensors 2019, 19(2), 286; https://doi.org/10.3390/s19020286

Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

1
Research Group Ideia Geoca Quito, Salesian Polytechnic University Engineer Systems, Quito 170131, Ecuador
2
Applied Artificial Intelligence Group, Carlos III University, Madrid 28270, Spain
*
Author to whom correspondence should be addressed.
This paper is an extended version of our paper published in: Padilla, W.R.; Jesús, G.H.; Molina, J.M. Model learning and spatial data fusion for predicting sales in local agricultural markets. In Proceedings of the 21st International Conference on Information Fusion (FUSION), Cambridge, UK, 10–13 July 2018.
Received: 31 October 2018 / Revised: 7 January 2019 / Accepted: 8 January 2019 / Published: 12 January 2019
(This article belongs to the Special Issue Data and Information Fusion for Wireless Sensor Networks)
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Abstract

In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered. View Full-Text
Keywords: predictive analysis; data mining; alternative circuits of commercialization; association rules; time series; spatial prediction; kriging and co-kriging predictive analysis; data mining; alternative circuits of commercialization; association rules; time series; spatial prediction; kriging and co-kriging
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Padilla, W.R.; García, J.; Molina, J.M. Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets. Sensors 2019, 19, 286.

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