Financial data modeling by using asynchronous parallel evolutionary algorithms
Wang Chun, Li Qiao-yun · Wuhan University Journal of Natural Sciences · 2003
In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.