Evolutionary collective behavior decomposition model for time series data mining

• Collective behaviors are modeled as aggregations of individual behaviors.
• Individual behavior is modeled by the minority game.
• Parameters of individual behavior can be learned using genetic algorithms.
• The new model is tested based on real-world financial data.

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.

The generative process for collective data. All agents are divided into KN + KJ + 1 groups where agents in the same subgroups act identically based on the strategy they follow. The collective data can be regarded as an aggregation of all agents’ actions. Figure optionsDownload as PowerPoint slide