Work

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

Year: 2025

Type: article

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.

Source: Agriculture

DOI: 10.3390/agriculture15222362

Authors: Sebastian Gonzalez Aguilera, Amir Karbassi Yazdi

Institution: University of Tarapacá

Language: English

Topic: Economics of Agriculture and Food Markets

Subfield: Economics and Econometrics

Field: Economics, Econometrics and Finance

Domain: Social Sciences

SDG: Zero hunger

Open Access status: gold

Estimated APC paid: $3,137