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Extra Virgin Olive Oil Price Prediction from Multi-source Variables and Machine Learning

Authors:
Juan Jose Cubillas
Angel Calle
Maria Isabel Ramos
Ruth Cordoba

Keywords: EVOO Price; Machine Learning Algorithms; Multisource Data.

Abstract:
This research underscores the vital need for accurate Extra Virgin Olive Oil (EVOO) price prediction, especially in Andalusia, Spain, given its significant economic and social impact on inflation, trade, and stability. Anticipating price fluctuations benefits producers, distributors, consumers, and governments for improved planning. The complexity arises from diverse influencing factors like climate, global markets, energy costs, and policies, highlighted by recent price surges due to adverse conditions. The study aims to develop a machine learning approach using historical and current data from official sources, processed with machine learning algorithms and Oracle Data Mining. The promising results demonstrate the feasibility of enhancing prediction accuracy, potentially stabilizing markets, optimizing distribution, and improving agricultural budgeting. Furthermore, this work contributes to advancing predictive modeling research within the agricultural sector.

Pages: 23 to 26

Copyright: Copyright (c) IARIA, 2025

Publication date: May 18, 2025

Published in: conference

ISSN: 2308-393X

ISBN: 978-1-68558-269-2

Location: Nice, France

Dates: from May 18, 2025 to May 22, 2025