Machine Learning Applied to Peruvian Vegetables Imports

2023/01/08 by Hugo Ticona-Salluca, Ticona-Salluca, Hugo, Fred Torres‐Cruz +3
Agricultural and Biological Sciences · Economics, Econometrics and Finance · #Agricultural and Food Production Studies #Business, Innovation, and Economy #FOS: Computer and information sciences #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2301.03587

openalex publication_date 2023/01/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The current research work is being developed as a training and evaluation object. the performance of a predictive model to apply it to the imports of vegetable products into Peru using artificial intelligence algorithms, specifying for this study the Machine Learning models: LSTM and PROPHET. The forecast is made with data from the monthly record of imports of vegetable products(in kilograms) from Peru, collected from the years 2021 to 2022. As part of applying the training methodology for automatic learning algorithms, the exploration and construction of an appropriate dataset according to the parameters of a Time Series. Subsequently, the model with better performance will be selected, evaluating the precision of the predicted values so that they account for sufficient reliability to consider it a useful resource in the forecast of imports in Peru.

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