The multi-agent system for prediction of financial time series

Authors:
Šarūnas Raudys;Indre Zliobaite
Affiliations:
Institute of Mathematics and Informatics, Dept. of Informatics, MIF, Vilnius University, Estland;Vilnius, Lithuania
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 1
Cited 6

Prediction of commodity prices in rapidly changing environments

ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I

Multi-agent System Approach to React to Sudden Environmental Changes

MLDM '07 Proceedings of the 5th international conference on Machine Learning and Data Mining in Pattern Recognition
Adaptive Ensemble Models of Extreme Learning Machines for Time Series Prediction

ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Price prediction in sports betting markets

MATES'10 Proceedings of the 8th German conference on Multiagent system technologies
Multiagent bayesian forecasting of structural time-invariant dynamic systems with graphical models

International Journal of Approximate Reasoning
A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition

Expert Systems with Applications: An International Journal
Using a case-based reasoning approach for trading in sports betting markets

Applied Intelligence

Quantified Score

Hi-index	0.00

Visualization

Abstract

To take into account different character of distinct segments of non-stationary financial time series the multi-agent system based forecasting algorithm is suggested. The primary goal of present paper is to introduce methodological findings that could help to reduce one step ahead forecasting error. In contrast to previous investigation [6], instead of single prediction rule we use a system of several adaptive forecasting agents. The agents evolve, compete among themselves. Final decision is made by a collective of the most successive agents and present time moment. New multi-agent forecasting system allows utilizing shorter training sequences and results in more accurate forecasts than employing single prediction algorithm.