Anomaly Detection and Regional Clustering in Chilean Wholesale Fruit and Vegetable Prices with Machine Learning

Sebastian Gonzalez Aguilera and Amir Karbassi Yazdi ()
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Sebastian Gonzalez Aguilera: Departamento de Ingeniería, Industrial y de Sistemas, Facultad de Ingeniería, Universidad de Tarapacá, Arica 1000000, Chile
Amir Karbassi Yazdi: Departamento de Ingeniería, Industrial y de Sistemas, Facultad de Ingeniería, Universidad de Tarapacá, Arica 1000000, Chile

Agriculture, 2025, vol. 15, issue 22, 1-20

Abstract: These days, the prices of fruits and vegetables fluctuate significantly, causing issues in the supply chain and for perishable products. This study aimed to use hybrid machine learning methods to cluster regional Chilean produce from 2015 to 2023 based on market analysis and address fluctuations in price and demand for agricultural products. The hybrid model employed in this research included substantial noise reduction with interquartile range (IQR), Z-score, and Hampel filters; temporal-spectral feature extraction through additive decomposition and Fast Fourier Transform (FFT); principal component analysis (PCA) for reducing dimensions; Gaussian mixture models (GMMs) for probabilistic clustering; and regime-shift detection using cumulative sum (CUSUM) and Bayesian online change-point detection (BOCPD). Finally, a sensitivity analysis demonstrated the approach’s reliability and robustness. The novelty of this research lies in the introduction of a hybrid model for forecasting agricultural product prices across Chile’s various regions.

Keywords: machine learning; agriculture productions; Chile; price (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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