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Authors: Leonid Galchynsky and Andriy Svydenko

Affiliation: Ihor Sikorsky Kyiv Polytechnic Institute, Ukraine

ISBN: 978-989-758-247-9

ISSN: 2184-4992

Keyword(s): Multi-agent Models, Oligopolistic Market, Neuron Net, Retail Prices, Petroleum Products, Short–term Forecasting.

Related Ontology Subjects/Areas/Topics: Agents ; Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Industrial Applications of Artificial Intelligence ; Intelligent Agents ; Internet Technology ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods ; Web Information Systems and Technologies

Abstract: In this study, we develop a multi-agent system model for the purpose of predicting the behaviour of petroleum product prices using short-term forecasting. Having analysed the issue, we found that the ability of multi-agent models to describe the behaviour of individual market agents along with with the oligopolistic nature of the market makes it possible to describe a long-term cooperation of agents. But the accuracy of short-term price predictions for the multi-agent model is insufficient. According to our hypothesis, this is caused primarily due to the nature of the agent’s heuristic algorithm as well as taking the price indices as the sole input. The accuracy of the price forecast for the multi-agent model in the short term is somewhat inferior to co-integration models and forecasting models based on neural networks that use historical price data of petroleum products. In this paper we have studied a hybrid model containing a certain set of agents, their price reaction is based on the neural network training process for each agent. With this approach it is possible to consider not just the price data from the past, but also such factors as potential threats and market destabilisation. Result comparison between the price obtained through our short-term forecast model and real data shows the former’s advantage over pure multi-agent models, co-integration models and over models forecasting based on neural networks. (More)

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Paper citation in several formats:
Galchynsky, L. and Svydenko, A. (2017). The Multiagent Model for Predicting the Behaviour and Short-term Forecasting of Retail Prices of Petroleum Products.In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS, ISBN 978-989-758-247-9, ISSN 2184-4992, pages 632-637. DOI: 10.5220/0006361706320637

@conference{iceis17,
author={Leonid Galchynsky. and Andriy Svydenko.},
title={The Multiagent Model for Predicting the Behaviour and Short-term Forecasting of Retail Prices of Petroleum Products},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,},
year={2017},
pages={632-637},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006361706320637},
isbn={978-989-758-247-9},
}

TY - CONF

JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 3: ICEIS,
TI - The Multiagent Model for Predicting the Behaviour and Short-term Forecasting of Retail Prices of Petroleum Products
SN - 978-989-758-247-9
AU - Galchynsky, L.
AU - Svydenko, A.
PY - 2017
SP - 632
EP - 637
DO - 10.5220/0006361706320637

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