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Authors: Ioannis Dionissopoulos 1 ; Fotis Assimakopoulos 2 ; Dimitris Spiliotopoulos 2 ; Dionisis Margaris 3 and Costas Vassilakis 1

Affiliations: 1 Department of Informatics and Telecommunications, University of the Peloponnese, Tripoli, Greece ; 2 Department of Management Science and Technology, University of the Peloponnese, Tripoli, Greece ; 3 Department of Digital Systems, University of the Peloponnese, Sparta, Greece

Keyword(s): Agricultural Products, Price Forecasting, Machine Learning, Deep Learning, Data Integration, Forecasting Models.

Abstract: This work focuses on the prediction of agricultural product and supply prices using historical data and artificial intelligence methods. Agricultural product and supply prices are important for the economy and growth of agriculture. Using modern data analysis and deep learning methods, a forecasting model was developed to help us predict future price trends. The data used include the sales prices of crop products and the purchase prices of agricultural inputs. The developed forecasting methods exhibit high accuracy for predicting the actual prices of products and supplies, with error margins ranging from 0.29% to 9.8%, while they can also predict price rises and falls, with respective success rates ranging from 73.29% to 84.96%.

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Paper citation in several formats:
Dionissopoulos, I.; Assimakopoulos, F.; Spiliotopoulos, D.; Margaris, D. and Vassilakis, C. (2024). Predicting Agricultural Product and Supplies Prices Using Artificial Intelligence. In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS; ISBN 978-989-758-716-0; ISSN 2184-3228, SciTePress, pages 371-379. DOI: 10.5220/0013071600003838

@conference{kmis24,
author={Ioannis Dionissopoulos. and Fotis Assimakopoulos. and Dimitris Spiliotopoulos. and Dionisis Margaris. and Costas Vassilakis.},
title={Predicting Agricultural Product and Supplies Prices Using Artificial Intelligence},
booktitle={Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS},
year={2024},
pages={371-379},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013071600003838},
isbn={978-989-758-716-0},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KMIS
TI - Predicting Agricultural Product and Supplies Prices Using Artificial Intelligence
SN - 978-989-758-716-0
IS - 2184-3228
AU - Dionissopoulos, I.
AU - Assimakopoulos, F.
AU - Spiliotopoulos, D.
AU - Margaris, D.
AU - Vassilakis, C.
PY - 2024
SP - 371
EP - 379
DO - 10.5220/0013071600003838
PB - SciTePress

- Science and Technology Publications, Lda.
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