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Exploring current trends in agricultural commodities forecasting methods through text mining: Developments in

Luana Gonçalves Guindani¹, Gilson Adamczuk Oliveirai¹, Matheus Henrique Dal Molin Ribeiro¹

¹Industrial & Systems Engineering Graduate Program (PPGEPS), Federal University of Technology - Parana (UTFPR), Via Do Conhecimento, KM 01 - Fraron, Pato Branco, PR, 85503-390, Brazil.

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Summary

This study identifies key forecasting methods for agribusiness time series, with machine learning hybrid and statistical models being most prevalent. It aids in decision-making by highlighting literary gaps in agricultural commodity forecasting.

Keywords:

Agribusiness Agricultural commodities Forecasting Latent dirichlet allocation Machine learning Text mining

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Area of Science:

Agricultural Economics
Data Science
Bibliometrics

Background:

Agriculture is a global economic driver facing supply chain risks.
Mathematical models are crucial for forecasting in agribusiness management.
Uncontrollable factors necessitate robust risk management strategies.

Purpose of the Study:

To automate topic identification in agribusiness forecasting research.
To construct a bibliographic portfolio of relevant studies from 2015-2022.
To analyze and categorize forecasting methodologies in agricultural commodity analysis.

Main Methods:

Systematic bibliometric analysis combined with Latent Dirichlet Allocation (LDA).
Categorization of 30 articles based on prediction model types: machine learning (ML), ML-NN, ML-Ensemble, ML-hybrid, and statistical.
Focus on methodologies applied in temporal analysis of agricultural commodities.

Main Results:

The dominant topic identified was "Forecasting Methods Applied to Agribusiness Time Series."
Machine learning hybrid (41.95%) and statistical (29.31%) models were the most utilized.
Machine learning with neural networks (ML-NN) followed at 14.94%.

Conclusions:

Identified literary gaps in forecasting methods for agribusiness.
Provided practical insights into forecasting methodologies for improved decision-making.
Highlighted the prevalence of hybrid and statistical approaches in agricultural time series analysis.