Improving Forecasting Using Information Fusion in Local Agricultural Markets

Padilla, Washington R.; García, Juan Francisco Blanco; Molina, José Manuel

doi:10.1007/978-3-319-92639-1_40

Cited by 2 publications

(2 citation statements)

References 14 publications

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“…In the second work [11], it is the set of products obtained from the application of the Apriori algorithm, inside an improvement that oscillates in a value between twenty and thirty percent, the estimates of future sales using time series considering the half-squared error, and this work establishes scenarios regarding the periodicity of a product.…”

Section: Related Workmentioning

confidence: 99%

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Padilla¹,

García²

2018

Computational Intelligence and Neuroscience

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This work proposes a methodology that reduces the error of future estimations in commercialization based on multivariate spatial prediction techniques (cokriging) considering the products with strong associations. It is based on the Apriori algorithm to find association rules in sales of agricultural products of local markets. Results show the improvement in spatial prediction accuracy after using the best association rules.

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Section: Related Workmentioning

confidence: 99%

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Padilla¹,

García²

2018

Computational Intelligence and Neuroscience

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“…An average of 300 items per week were sold. This research is based on previous work on information fusion and data mining techniques [2] and the ability to extract knowledge from the fusion of information [3]. Some preliminary results were presented by the same authors at the 2018 International Conference of Information Fusion, Cambridge, UK, on 10–13 July 2018 [4].…”

Section: Introductionmentioning

confidence: 99%

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

Padilla¹,

García

Molina

2019

Sensors

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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.

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Improving Forecasting Using Information Fusion in Local Agricultural Markets

Cited by 2 publications

References 14 publications

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

Data Association Methodology to Improve Spatial Predictions in Alternative Marketing Circuits in Ecuador

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

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