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Agent-based distributed time series forecasting system

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Many studies have demonstrated that agent-based distributed computing improves quality of distributed computations. In this paper, self-aware software agents are used to manage the distributed computations in order to improve effectiveness of investment decisions. A distributed time series forecasting approach based on the modified Group Method Data Handling (GMDH) method and agent oriented programing is proposed. The forecasted results computed by agents are used to make an investment decision. To assess the effectiveness of the system, we used the time series of EUR/USD currency pair stock prices. The empirical results with a real data set clearly suggest that the system can be deployed on the trading platform to automate process of the prediction of financial markets.
Rocznik
Strony
17--27
Opis fizyczny
Bibliogr. 12 poz., rys.
Twórcy
autor
  • Faculty of Computer Science and Information Technology, West Pomeranian University of Technology, Szczecin, Poland
Bibliografia
  • [1] Wiliński A., GMDH – metody grupowania argumentów w zadaniach zautomatyzowanej predykcji zachowań rynków finansowych, Warszawa - Szczecin 2009, 278 s., ISBN 9788389475237
  • [2] GMDH - Group Method of Data Handling, http://www.gmdh.net/
  • [3] Ivakhnenko A., Ivakhnenko G., Problems of Further Development of the Group Method of Data Handling Algorithms, Part I. Pattern Recognition and Image Analysis vol.10 No.2, pp. 187-194, 2000.
  • [4] Ivakhnenko A., Ivakhnenko G.,Mueller J., Self-organization of Neural Network with Active Neurons, Part I. Pattern Recognition and Image Analysis, vol.4 No.2, pp. 185-196, 1999.
  • [5] Ivakhnenko A.G., Ivakhnenko G.A., Andrienko N.M. Inductive Computer Advisor for Current forecasting of Ukraine's Macroeconomy, Systems Analysis Modelling Simulation, 22, no.1, 1998.
  • [6] Ivakhnenko G.A., Model-Free Analogues As Active Neurons for Neural Networks Self-Organization, Control Systems and Computers, no.2, p.100-107, 2003.
  • [7] Madala H.R., Ivakhnenko A.G., Inductive Learning Algorithms for Complex Systems Modelling. CRC Press Inc.. Boca Raton, Ann Arbor, London, Tokyo, ISBN: 0-8493-4438-7, 1994
  • [8] Rogoza, V., Zabłocki, M., Grid computing and Cloud computing in scope of JADE and OWL based Semantic Agents – A Survey, Przegląd Elektrotechniczny, 90, 2/2014, ISSN 0033-2097
  • [9] Bouška J., Kordík P., Time Series Prediction by means of GMDH Analogues Complexing and GAME (Paper in Conference Proceedings), In IWIM 2007 - International Workshop on Inductive Modelling. Praha: Czech Technical University in Prague, 2007, p. 278-287. ISBN 9788001038819
  • [10] Samsudin R., Saad P., Shabri A., A hybrid least squares support vector machines and GMDH approach for river flow forecasting, Hydrol. Earth Syst. Sci. Discuss., 7, 3691-3731, doi:10.5194/hessd-7-3691-2010, 2010
  • [11] Kondo T., Kondo Ch., Takao S., Ueno J., Feedback GMDH-type neural network algorithm and its application to medical image analysis of cancer of the liver, Artificial Life and Robotics, Volume 15, Issue 3, p. 264-269, 2010
  • [12] Yousefpour A., Ahmadpour Z., The prediction of air pollution by using Neuro-fuzzy GMDH, The Journal of Mathematics and Computer Science, Vol .2, No.3, p. 488-494, 2011.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-91aeed8e-a390-45e4-bb7e-4dd5bf71acb0
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