Evolutionary collective behavior decomposition model for time series data mining |
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| Affiliation: | 1. Intelligent Computing and Machine Learning Lab, School of ASEE, Beihang University, Beijing 100191, China;2. School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China;3. Department of Computer Science, University of British Columbia, Canada;4. Courant Institute of Mathematical Sciences, New York University, USA;1. Audio & Speech Processing Lab, Computer Engineering Department, Iran University of Science & Technology, Tehran, Iran;2. Electrical and Computer Engineering Department, K.N. Toosi University of Technology, Tehran, Iran;1. Department of Computer Science, East China University of Political Science and Law, Shanghai 201620, China;2. Department of Computer Science, University College London (UCL), London WC1E 6BT, UK |
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| Abstract: | In this research, we propose a novel framework referred to as collective game behavior decomposition where complex collective behavior is assumed to be generated by aggregation of several groups of agents following different strategies and complexity emerges from collaboration and competition of individuals. The strategy of an agent is modeled by certain simple game theory models with limited information. Genetic algorithms are used to obtain the optimal collective behavior decomposition based on history data. The trained model can be used for collective behavior prediction. For modeling individual behavior, two simple games, the minority game and mixed game are investigated in experiments on the real-world stock prices and foreign-exchange rate. Experimental results are presented to show the effectiveness of the new proposed model. |
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| Keywords: | Minority games Mixed games Collective behavior decomposition Genetic algorithms Evolutionary mixed games learning |
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