Abstract
In this research, it was designed a methodology to forecast prices of products In the Mexican Republic. The dataset is composed of basic basket products. The work consists of analyzing open and mixed data of this dataset. The approach is centered on studying how is the behavior in time and location domains for three products, tuna, detergent, and milk. The data ranges for five years. Neural networks were used to analyze data, and several experiments of price forecast were issued using different granularity levels. The regression models were validated using two traditional approaches of the machine learning area, coefficient of determination, and mean absolute error. The experiments showed that the price of basic products varies by zone and it is possible to give a forecast with a percentage of 80% of precision.
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Millán, P., Mata, F. (2019). Analytics for Basic Products in Mexico. In: Mata-Rivera, M., Zagal-Flores, R., Barría-Huidobro, C. (eds) Telematics and Computing. WITCOM 2019. Communications in Computer and Information Science, vol 1053. Springer, Cham. https://doi.org/10.1007/978-3-030-33229-7_11
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DOI: https://doi.org/10.1007/978-3-030-33229-7_11
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