Authors:
Leonid Galchynsky
and
Andriy Svydenko
Affiliation:
Ihor Sikorsky Kyiv Polytechnic Institute, Ukraine
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.
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